# Predicting Fear of Breast Cancer Recurrence in women five years after diagnosis using Machine Learning and healthcare reimbursement data from the French nationwide VICAN survey

Mamoudou Koume, Lorène Seguin, Julien Mancini, Marc-Karim Bendiane, Anne-Déborah Bouhnik, Raquel Urena

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## To cite this version:

Mamoudou Koume, Lorène Seguin, Julien Mancini, Marc-Karim Bendiane, Anne-Déborah Bouhnik, et al.. Predicting Fear of Breast Cancer Recurrence in women five years after diagnosis using Machine Learning and healthcare reimbursement data from the French nationwide VICAN survey. International Journal of Medical Informatics, 2024, 193, pp.105705. 10.1016/j.ijmedinf.2024.105705. inserm-05031130

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# Predicting Fear of Breast Cancer Recurrence in women five years after diagnosis using Machine Learning and healthcare reimbursement data from the French nationwide VICAN survey ☆

Mamoudou Koume a, Lorène Seguin c, Julien Mancini a,b, Marc-Karim Bendiane a,
Anne-Déborah Bouhnik a,*, Raquel Urena a

## ARTICLE INFO

**Keywords:**

Fear of cancer recurrence

Machine learning

Healthcare reimbursement data

Breast cancer

## ABSTRACT

**Objective:** A major concern for cancer survivors after treatment is the Fear of Cancer Recurrence (FCR), which is the fear that cancer will reappear or progress. This fear can be exacerbated by medical uncertainty about the future, leading to harmful obsession and having a negative impact on quality of life. This study aims to develop a predictive Machine Learning (ML) model using healthcare reimbursement data to better predict FCR and understand the factors influencing FCR in women with breast cancer five years after their diagnosis.

**Materials and Methods:** We used data from the VICAN (VIe après le CANcer) survey to propose an interpretable model to identify patients at risk of developing clinical FCR. The reimbursement data for each patient were analyzed beyond the first two years of treatment, excluding the initial phase influenced by the cancer curative therapeutic process. Data experiments were conducted, including the use of algorithms such as Random Forest, Support Vector Machines, Gradient Boosting, eXtreme Gradient Boosting, and Multilayer Perceptron. The AUC was used to choose the optimal model.

**Results:** The dataset is composed of 918 patients classified regarding FCR. The experimental design incorporated classification levels of medications, biological and medical procedures. Subsequently, data was generated for two experiments, facilitating examination at the ultimate healthcare classification level in Experiment 1, while Experiment 2 targeted the penultimate classification level. Overall, the best-performing model achieved an AUC of 66%. This demonstrates some effectiveness of the algorithms in detecting patients at risk of developing clinical FCR and encourages further investigations to enhance the model's performance and assess its generalizability.

**Conclusion:** ML applied to reimbursement data has shown promise in predicting FCR, aiding in the identification of patients at risk of developing it. The results pave the way for personalized prevention and intervention strategies, representing a significant advancement in postcancer care focusing on the needs of survivors.

## 1. Introduction

Breast cancer (BC) is the predominant cancer and the leading cause of cancer-related mortality in French women. Therapeutic advances and early screenings have significantly increased life expectancy in recent

decades [1,2], providing new life perspectives for people affected by this disease. Cancer diagnosis not only initiates a battle against the disease but also casts a pervasive shadow of fear and anxiety, particularly with regards to the prospect of recurrence at middle and long time after treatment. Commonly referred to as Fear of Cancer Recurrence (FCR), it

<footnote>a Aix Marseille Univ, Inserm, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, ISSPAM, Marseille, France</footnote>

<footnote>b Aix Marseille Univ, APHM, Inserm, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, ISSPAM, BioSTIC, Biostatistique et Technologies de l'Information et de la Communication, Marseille, France</footnote>

<footnote>c Aix-Marseille Univ, Institut Paoli-Calmettes, Département d'Oncologie Médicale, Marseille, France</footnote>

<footnote>∗ Abbreviations: FCR (Fear of Cancer Recurrence); ML (Machine Learning); BC (Breast Cancer); AUC (Area Under the Curve); MEDIC (Medication); BIO (Biological procedures); CCAM (Medical procedures), RF (Random Forest), GB (Gradient Boosting), XGB (eXtreme Gradient Boosting), SVM (Support Vector Machines), MLP (MultiLayer Perceptron).</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: anne-deborah.bouhnik@univ-amu.fr (A.-D. Bouhnik).</footnote>
is defined as “the fear or worry that cancer will come back or progress in the same organ or in another part of the body” [3]. While some degree of FCR may be considered as a normal response to the possibility of recurrence, it can be exacerbated in some survivors by medical uncertainty about the future, becoming obsessive and detrimental with a negative impact on the quality of life of these individuals [4]. Previous studies have highlighted the persistent nature of FCR and its impact on the psychosocial well-being of cancer survivors [5,6]. While clinical factors associated with the risk of recurrence in localized BC are now clearly established, including aspects such as age, tumor size, grade, lymph node involvement, and histological response to chemotherapy, the subjective perception of FCR by patients, particularly five years after diagnosis, remains an aspect requiring further exploration [7,8]. This study aims to bridge this gap by harnessing the power of Machine Learning (ML) and tapping into comprehensive healthcare reimbursement data obtained from VICAN (Vie après le CANcer), a large-scale French nationwide survey.

Furthermore, Artificial Intelligence (AI) techniques have recently been implemented to develop models of progression and survival of BC, demonstrating the potential of combined semi-explainable ML approaches to provide an explanation of how demographic, clinical, and biological data are important in predictions [9]. Other recent publications related to AI specifically employ these techniques with the aim of modeling either cancer risk or patient outcomes, using approaches such as neural networks [10–12]. These approaches are still underutilized, and to our knowledge, they have never been applied to non-clinical outcomes, such as quality of life, or other measures of subjective health. The same is true for the identification of FCR in cancer survivors, although the latter has been shown to be associated with medical overconsumption in early-stage BC survivors [13]. Research has shown that FCR is associated with increased Health Care Utilization (HCU) among early-stage BC survivors. Specifically, a study involving 300 early-stage BC survivors demonstrated that FCR predicted more visits to both oncology providers and primary care providers, as well as more phone calls to oncology providers. This increased HCU, particularly excessive medical reassurance-seeking, can lead to unnecessary medical costs. Additionally, an increase in the utilization of primary healthcare services among women with a history of BC, especially among the elderly, has been demonstrated in [14].

In this article, we analyzed healthcare reimbursement data spanning from 2011 to 2016 for patients diagnosed with primary BC between 2009 and 2011 in France over a two-year period following their diagnosis. Our hypothesis is that FCR is associated with anxiety and medical overconsumption, which may manifest as frequent use of medications used to treat anxiety, as well as overconsumption of biological and medical procedures. This study is purely methodological and helps to develop a predictive model that not only identifies patterns within healthcare reimbursement data but also facilitates a deeper understanding of the nuanced predictors of FCR, empowering healthcare professionals to implement targeted interventions and support mechanisms for long-term cancer survivors.

# 2. Materials and methods

## 2.1. VICAN-5 survey

The VICAN-5 survey, conducted five years after the initial cancer diagnosis, encompasses a nationally representative sample of 4,174 adult survivors living in France, aged 18-82 years at their first cancer diagnosis, and affiliated with one of the three main French health insurance schemes (covering over 90% of the population) [15]. Each patient's tumor site was recorded using the corresponding International Classification of Diseases-10 (ICD-10) code. The cohort was limited to 12 cancer sites: breast (C50), lung (C3, C34), colorectal (C18-C20), prostate (C61), upper aerodigestive tract (VADS) (C00-C14, C30-32), bladder (C67), melanoma (C43), thyroid (C73), kidney (C64-C66), endometrial (C54),

cervical (C53), and non-Hodgkin lymphoma (C83), representing 88% of cancer incidence in France [15]. Through telephone interviews conducted five years after the cancer diagnosis, VICAN-5 explored various aspects of survivors' life conditions, such as access to care, recovery after treatment, and the impact of the disease on the personal and professional lives of affected individuals [15].

## 2.2. Study population

In the VICAN-5 survey, our focus lies on women who have been diagnosed with nonmetastatic BC and have shown no signs of disease recurrence or progression five years after diagnosis. Five-year disease progression post-diagnosis was a key measure of disease severity in [15]. It was determined using SNIIRAM healthcare data, defining disease progression as the occurrence of metastases, new cancer diagnoses, palliative care admissions, or receiving cancer treatments at least 24 months post-diagnosis [15]. Of this cohort, 930 actively participated in the VICAN-5 survey, which included collecting survey data through telephone interviews, along with information related to the outcome of FCR, as well as healthcare consumption, including medications, procedures, lab tests, etc. The inclusion criteria encompass patients without metastatic cancer, those who have not experienced recurrence or progression of their disease. As a result, 918 patients were included in the study. The flow chart that specifies the patient inclusion criteria is provided in Fig. 1. BC data summary is provided in the Supplementary Materials (Section 4).

## 2.3. Fear of cancer recurrence prediction

The aim of predicting FCR is to identify, based on a set of healthcare reimbursement data, patients at risk of developing clinical FCR as shown in Fig. 2. The intricacy of a ML algorithm is closely linked to the dimensionality of input data, and within the specific context, challenges manifest as a result of the “curse of dimensionality”, as discussed by Altman et al. in [16]. To tackle this “curse of dimensionality” and enhance predictive performance, essential preprocessing steps, including statistical tests and meticulous feature selection, along with imbalanced data processing, are undertaken. We constructed a comprehensive pipeline that seamlessly integrates all preprocessing steps, data rebalancing, feature selection, model training, and evaluation. See details in the Supplementary Materials (Section 5).

# 3. Results

## 3.1. Statistical analysis

Table 1 provides a comprehensive snapshot of the patient information of the entire study population, allowing for a detailed examination of various factors related to FCR (0 = Non-FCR and 1 = FCR). More information on the description of healthcare data is provided in the Supplementary Materials (Section 1.4).

## 3.2. Experimental results

Two experiments were conducted, leveraging prior knowledge extracted from the existing coding hierarchy of each healthcare reimbursement table—MEDIC, BIO, and CCAM. The first experiment focused on the ultimate level of healthcare classification, while the second one addressed the penultimate level of classification. Notably, medications and medical procedures are classified into five levels, whereas biological procedures have three levels of classification. Features in each experimental setting are detailed in the Supplementary Materials (Section 1.2). In each experiment, we employed ML models incorporating oversampling and feature selection techniques. The comprehensive results can be found in Table 2 and Fig. 3.
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Flow diagram of included patients. Medical data were collected through a questionnaire from caregivers who initiated cancer treatment. Additionally, information on healthcare reimbursement from diagnosis date to five years after, including hospital and outpatient care, as well as prescribed medications eligible for reimbursement, was extracted from the SNIIRAM (National Health Insurance Information System in France), not only for VICAN-5 participants but also for all people selected to participate in that survey, totaling 31,637 individuals [15]. In the VICAN-5 survey, among participants who did not report any relapse, FCR was measured using the following question: "How often do you think about the possibility of the disease recurring?" with responses on a 5-point Likert scale: never/a few times a month/a few times a week/a few times a day/several times a day [15]. The answers were categorized into two classes: Non-FCR (never) and FCR (all other responses).</figcaption></figure>

### 3.3. SHAP-based interpretation

Fig. 4 showcases a left-side plot illustrating the SHAP summary and a right-side plot displaying the summary local explanation of the top 20 features' importance ranking for a GB model using SMOTE, SFM, and Experiment 2 settings. For the left-side plot, where all features are considered collectively to understand their overall impact on model predictions in terms of mean absolute SHAP values, key features like *Acidity problems*, *Echography*, and *Spinal column radiography* are highlighted, each boasting prominently elevated mean absolute SHAP values. These values highlight substantial contributions to the model's predictions, emphasizing the pivotal roles in shaping the decision-making process of the FCR predictive model. Additionally, features such as *Benzodiazepine derivatives*, *Anilides* (which are among the analgesics that block pain signals in the brain), and *Radiology procedures* exhibit noteworthy high mean absolute SHAP values. Furthermore, features like *Puncture*, *Biopsy*, and *Sampling of the female genital system*, *Glucocorticoids*, *Vitamin D* and analogues, *NSAIDs*, *Breast radiography*, *Standard blood biochemistry*, *Broad-spectrum penicillins*, *Imaging procedures of the musculoskeletal system without topographical precision*, *Breast remnography* [MRI], and *Standard*

hormone assays also play significant roles. Their SHAP values contribute comprehensively to the model's overall predictive performance for FCR. Conversely, the lower mean absolute SHAP value for standard hormone assays suggests a comparatively smaller impact on the model's output. This collective analysis offers a nuanced perspective on the varying degrees of importance among different medical features, providing valuable insights into their respective contributions to the model's overall predictive performance.

For the right-side plot, the color of the SHAP values signifies the positive and negative impact of the sample on the predicted outcome. Each feature is color-coded to denote its effect: high values are highlighted in red hues, while low values are marked in blue hues. It presents a comprehensive insight into the nuanced impact of various medical features on the model's predictions for the FCR. Within this local impact assessment, some features stand out with distinct effects. Notably, higher values for drugs related to *Acidity problems*, *Echography*, *Spinal column radiography*, *Radiology*, and *Benzodiazepine derivatives* exhibit positive local impacts, emphasizing their role in contributing towards class FCR. Lower values for *Glucocorticoids*, *Vitamines D* and analogues, and *NSAIDs* show posi-
<figure><img src="image_6.png" /><figcaption>Fig. 2. High-level flow diagram of Fear of Cancer Recurrence prediction framework.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3. The Receiver Operating Characteristic (ROC) curves using Experiment 2 settings with 50 repeats using GB model, SFM and SMOTE. The robustness of our findings is highlighted by the repetition of GB model 50 times in Experiment 2 settings. The resultant set comprises all 50 ROC curves, the mean ROC curve, and the standard deviation, providing a comprehensive overview of model performance variability, emphasizing the consistency and reliability of the GB model across multiple iterations.</figcaption></figure>

tive local impacts, indicating their potential positive contribution to the likelihood of the instance being classified as FCR, and vice versa. Additionally, high values for *Standard blood biochemistry, Broad-spectrum penicillins, Imaging procedures of the musculoskeletal system*, and *Standard hormone assays* positively influence the FCR prediction at the local level, contributing towards the FCR class.

As shown in Fig. 4 (B) for the sample with a predicted FCR, high values for *QUANTITE_BIO* (value:133) and *Vitamin D Analogues* (value:14) are highlighted in blue, indicating their potential to increase the likelihood of FCR and consequently lead to correct classification. Higher

values and a red indicator for *Breast MRI* (value:1), *standard hormone assays* (value:21), *Liquid-based cytopathological examination* (value:3), and *Acidity problems* (value:7) indicate a positive impact on the output model and contribute to correct classification. For the sample with a predicted class Non-FCR in Fig. 4 (C), features like *Natural alkaloids of opium* (value:1), *VISITE_MEDIC* (value:23), and *Acetic acid derivatives and related compounds* (value:2) are red values, indicating that these features increase the SHAP value for this sample that make it more likely that this patient will continue to suffer from FCR and contribute towards class FCR. Since features like *Radiology* (value:1), *Hematology* (value:5),
<caption>**Table 1**</caption>

Characteristics of the study population, with qMEDIC (quantity of medication prescribed), qBIO (biological procedures), qCCAM (medical procedures), qHOSPIT (hospitalization records), and qDIAG (diagnostic procedures), stratified by FCR and Non-FCR groups. Age at diagnosis shows a significant difference, with the Non-FCR group diagnosed at an older age (52.5 vs. 48.8 years, p < 0.0001) compared to the FCR group. Furthermore, examining differences in qMEDIC, qBIO, qCCAM, qHOSPIT, and qDIAG between FCR and Non-FCR groups, offers valuable insights into additional distinctions. The FCR group shows significant higher values for qBIO and qCCAM than the Non-FCR group (38.7 versus 34.6, p = 0.036 and 24.9 versus 22.5, p = 0.008 for qBIO and qCCAM respectively). By contrast, no significant differences are observed in qMEDIC (p = 0.232), qHOSPIT (p = 0.273), and qDIAG (p = 0.444), suggesting similar patterns of medication consumption, hospitalization and diagnostic procedures between the two groups.

<table><thead><tr><th></th><th>Non-FCR<br>(N = 336)</th><th>FCR<br>(N = 582)</th><th>Pvalue<br>-</th></tr></thead><tbody><tr><td>Year of diagnosis</td><td></td><td></td><td>0.140</td></tr><tr><td>2009</td><td>44</td><td>86</td><td></td></tr><tr><td>2010</td><td>177</td><td>333</td><td></td></tr><tr><td>2011</td><td>115</td><td>163</td><td></td></tr><tr><td>Age at diagnosis (year)</td><td></td><td></td><td>&lt; 0.0001</td></tr><tr><td>Min-Max</td><td>23-80</td><td>27-80</td><td></td></tr><tr><td>Mean (SD)</td><td>52.5 (11.6)</td><td>48.8 (9.5)</td><td></td></tr><tr><td>Median [IQR]</td><td>49 [44-62]</td><td>48 [43-52]</td><td></td></tr><tr><td>qMEDIC</td><td></td><td></td><td>0.2317</td></tr><tr><td>Min-Max</td><td>0-1370</td><td>0-1313</td><td></td></tr><tr><td>Mean (SD)</td><td>186.1 (213.5)</td><td>169.5 (181.3)</td><td></td></tr><tr><td>Median [IQR]</td><td>109 [46.75-234.25]</td><td>101 [53-213.75]</td><td></td></tr><tr><td>qBIO</td><td></td><td></td><td>0.036</td></tr><tr><td>Min-Max</td><td>0-196</td><td>0-247</td><td></td></tr><tr><td>Mean (SD)</td><td>34.6 (28.1)</td><td>38.7 (30.2)</td><td></td></tr><tr><td>Median [IQR]</td><td>28 [14-45]</td><td>32 [18-51]</td><td></td></tr><tr><td>qCCAM</td><td></td><td></td><td>0.008</td></tr><tr><td>Min-Max</td><td>0-69</td><td>0-123</td><td></td></tr><tr><td>Mean (SD)</td><td>22.5 (12.7)</td><td>24.9 (14.6)</td><td></td></tr><tr><td>Median [IQR]</td><td>19 [14-31]</td><td>23 [15-32]</td><td></td></tr><tr><td>qHOSPIT</td><td></td><td></td><td>0.273</td></tr><tr><td>Min-Max</td><td>1-59</td><td>1-285</td><td></td></tr><tr><td>Mean (SD)</td><td>8.26 (11.2)</td><td>9.72 (23.1)</td><td></td></tr><tr><td>Median [IQR]</td><td>4[2-9]</td><td>4[2-8]</td><td></td></tr><tr><td>qDIAG</td><td></td><td></td><td>0.444</td></tr><tr><td>Min-Max</td><td>1-39</td><td>1-38</td><td></td></tr><tr><td>Mean (SD)</td><td>4.4 (4.3)</td><td>4.1 (4.4)</td><td></td></tr><tr><td>Median [IQR]</td><td>3[2-6]</td><td>3[2-5]</td><td></td></tr></tbody></table>

and *Spinal column radiography* (value:1) are highlighted in blue and have small values, indicating that they contribute to the FCR class.

## 4. Discussion

The present study aimed to develop a predictive ML model to discern patterns within healthcare reimbursement data and identify patients at risk of developing FCR in women with BC five years post-diagnosis. Considering the uniqueness of our dataset, which combines reimbursement data with patient-reported outcomes, there appears to be no prior study, to the best of the author's knowledge, that predicts FCR using this type of reimbursement data. Overall, the results of the predictive models indicate that ML applied to healthcare reimbursement data shows promise in predicting FCR, with the best-performing model achieving an AUC of 0.66, recall of 0.70 and precision of 0.71. While the AUC value demonstrates moderate discriminative power, the high recall implies that the model successfully identifies a large proportion of patients at risk of developing clinical FCR. Specifically, GB model consistently exhibits high recall values across experiments, indicating its effectiveness in capturing true positive cases of FCR. XGB and RF models also present competitive recall results, highlighting their potential utility in identifying patients at risk.

The findings of this study have several clinical implications. The developed ML models, particularly those with high recall rates, can aid healthcare providers in identifying patients at risk of developing FCR, enabling early intervention and personalized prevention strategies.

By leveraging healthcare reimbursement data, the model offers a non-invasive and cost-effective approach to FCR prediction, complementing existing clinical assessments. The SHAP-based interpretation allows to identify the medical consumption's most contributing to the predictive model, and among them those associated with higher probabilities of being at risk of FCR. They include imaging acts as echography, spinal column radiography, radiology, breast MRI and imaging procedures of the musculoskeletal system. Some drugs consumed are also identified as increasing the likelihood of FCR: drugs related to acidity problems, benzodiazepines, and broad-spectrum penicillins. Finally, standard blood biochemistry and standard hormone assays have also been find to be associated with higher probabilities of suffering from FCR.

These results may help physicians and particularly General Practitioners (GPs) to detect patients at risk of developing pathological FCR. In the same way, drugs related to acidity and benzodiazepines may be associated with anxiety and their consumption should alert on the possible psychological distress of the patient. This may be the same regarding the multiplicity of imaging acts and blood tests that may be a way to reassure patient on the possibility of cancer recurrence. Detecting the accumulation of medical consumption among non-metastatic BC women, with no recurrence or disease progression in the five years following diagnosis may be a way for GPs to identify patients at risk of FCR, to better inform them on long-term symptoms they may experience, and to orient them towards psychosocial interventions that aim to reduce FCR. This detailed examination holds potential significance for further-
<caption>**Table 2**</caption>

Comparative Performance: AUC, Recall, Precision and F1-score of Classifiers using Meta-Transformer-based Feature Selection (SFM), Recursive Feature Elimination (RFE), and Relief-based Algorithm (ReliefF) with SMOTE on one hand and ADASYN on the other hand. Overall, the ML algorithms yielded good results, with those from Experiment 2 settings performing the best. Ensemble models such as RF, GB, XGB, along with the neural network MLP, demonstrate a better balance, notably in terms of AUC and Recall, while SVM may benefit from more meticulous tuning. Among these models, GB emerged as the top performer, showcasing competitive performance across various evaluation metrics, particularly evident in Experiment 2 settings and effectively capturing positive instances, while maintaining a balanced precision, rendering it a noteworthy choice for the task at hand. In Experiment 1 settings, AUC ranges from 0.49 to 0.55, with SMOTE generally enhancing performance. Experiment 2 settings show AUC values between 0.57 and 0.66, with similar trends. GB excels in recall, capturing positive instances effectively. In Experiment 1 settings, recall varies from 0.50 to 0.67, with SMOTE consistently outperforming ADASYN. In Experiment 2 settings, recall ranges from 0.62 to 0.79, reinforcing GB's positive performance. Precision values for GB vary, indicating a trade-off with recall. In Experiment 1 settings, precision ranges from 0.61 to 0.65, and SMOTE impacts performance. The F1-score ranges from 0.55 to 0.66, with SMOTE generally improving balance. In Experiment 2 settings, precision varies from 0.65 to 0.72, and achieves a balanced F1-score between 0.64 and 0.74. The choice between SMOTE and ADASYN significantly impacts model performance, with SMOTE generally yielding superior results. Data balancing plays a pivotal role in enhancing outcomes. Furthermore, models demonstrate varying responses to feature selection methods, emphasizing the need to align these selections carefully for optimal performance.

<table><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th colspan="6">Experiment 1 settings</th><th colspan="6">Experiment 2 settings</th></tr><tr><th colspan="2">SFM</th><th colspan="2">RFE</th><th colspan="2">ReliefF</th><th colspan="2">SFM</th><th colspan="2">RFE</th><th colspan="2">ReliefF</th></tr><tr><th>SMOTE</th><th>ADASYN</th><th>SMOTE</th><th>ADASYN</th><th>SMOTE</th><th>ADASYN</th><th>SMOTE</th><th>ADASYN</th><th>SMOTE</th><th>ADASYN</th><th>SMOTE</th><th>ADASYN</th></tr></thead><tbody><tr><td rowspan="4">RF</td><td>AUC</td><td>0.53</td><td>0.55</td><td>0.52</td><td>0.53</td><td>0.51</td><td>0.51</td><td>0.62</td><td>0.63</td><td>0.64</td><td>0.62</td><td>0.61</td><td>0.59</td></tr><tr><td>Recall</td><td>0.50</td><td>0.46</td><td>0.44</td><td>0.40</td><td>0.61</td><td>0.62</td><td>0.62</td><td>0.61</td><td>0.60</td><td>0.61</td><td>0.68</td><td>0.74</td></tr><tr><td>Precision</td><td>0.64</td><td>0.68</td><td>0.61</td><td>0.63</td><td>0.65</td><td>0.63</td><td>0.70</td><td>0.70</td><td>0.72</td><td>0.70</td><td>0.69</td><td>0.68</td></tr><tr><td>F1-score</td><td>0.56</td><td>0.55</td><td>0.51</td><td>0.49</td><td>0.63</td><td>0.62</td><td>0.66</td><td>0.65</td><td>0.65</td><td>0.65</td><td>0.68</td><td>0.71</td></tr><tr><td rowspan="4">GB</td><td>AUC</td><td>0.50</td><td>0.49</td><td>0.53</td><td>0.51</td><td>0.55</td><td>0.51</td><td><strong>0.66</strong></td><td>0.62</td><td>0.64</td><td>0.61</td><td>0.59</td><td>0.57</td></tr><tr><td>Recall</td><td>0.56</td><td>0.50</td><td>0.54</td><td>0.52</td><td>0.67</td><td>0.65</td><td><strong>0.70</strong></td><td>0.62</td><td>0.77</td><td>0.64</td><td>0.79</td><td>0.74</td></tr><tr><td>Precision</td><td>0.62</td><td>0.61</td><td>0.62</td><td>0.62</td><td>0.65</td><td>0.62</td><td><strong>0.71</strong></td><td>0.66</td><td>0.72</td><td>0.65</td><td>0.65</td><td>0.66</td></tr><tr><td>F1-score</td><td>0.59</td><td>0.55</td><td>0.58</td><td>0.57</td><td>0.66</td><td>0.63</td><td><strong>0.71</strong></td><td>0.64</td><td>0.74</td><td>0.64</td><td>0.71</td><td>0.70</td></tr><tr><td rowspan="4">XGB</td><td>AUC</td><td>0.51</td><td>0.51</td><td>0.50</td><td>0.51</td><td>0.49</td><td>0.45</td><td>0.62</td><td>0.60</td><td>0.61</td><td>0.62</td><td>0.57</td><td>0.54</td></tr><tr><td>Recall</td><td>0.55</td><td>0.59</td><td>0.56</td><td>0.57</td><td>0.68</td><td>0.63</td><td>0.65</td><td>0.64</td><td>0.66</td><td>0.64</td><td>0.79</td><td>0.76</td></tr><tr><td>Precision</td><td>0.63</td><td>0.64</td><td>0.63</td><td>0.61</td><td>0.62</td><td>0.59</td><td>0.67</td><td>0.67</td><td>0.68</td><td>0.68</td><td>0.64</td><td>0.64</td></tr><tr><td>F1-score</td><td>0.59</td><td>0.61</td><td>0.59</td><td>0.59</td><td>0.65</td><td>0.61</td><td>0.66</td><td>0.65</td><td>0.67</td><td>0.66</td><td>0.71</td><td>0.69</td></tr><tr><td rowspan="4">MLP</td><td>AUC</td><td>0.45</td><td>0.56</td><td>0.45</td><td>0.51</td><td>0.53</td><td>0.54</td><td>0.61</td><td>0.61</td><td>0.50</td><td>0.58</td><td>0.64</td><td>0.66</td></tr><tr><td>Recall</td><td>0.62</td><td>0.32</td><td>0.44</td><td>0.38</td><td>0.60</td><td>0.52</td><td>0.69</td><td>0.46</td><td>0.93</td><td>0.55</td><td>0.66</td><td>0.63</td></tr><tr><td>Precision</td><td>0.61</td><td>0.70</td><td>0.59</td><td>0.63</td><td>0.64</td><td>0.67</td><td>0.67</td><td>0.76</td><td>0.64</td><td>0.69</td><td>0.74</td><td>0.75</td></tr><tr><td>F1-score</td><td>0.61</td><td>0.44</td><td>0.50</td><td>0.47</td><td>0.62</td><td>0.59</td><td>0.69</td><td>0.57</td><td>0.76</td><td>0.61</td><td>0.70</td><td>0.68</td></tr><tr><td rowspan="4">SVM</td><td>AUC</td><td>0.46</td><td>0.52</td><td>0.53</td><td>0.47</td><td>0.55</td><td>0.55</td><td>0.55</td><td>0.57</td><td>0.51</td><td>0.57</td><td>0.62</td><td>0.60</td></tr><tr><td>Recall</td><td>0.80</td><td>0.61</td><td>0.75</td><td>0.69</td><td>0.56</td><td>0.71</td><td>0.30</td><td>0.67</td><td>0.64</td><td>0.44</td><td>0.74</td><td>0.55</td></tr><tr><td>Precision</td><td>0.64</td><td>0.62</td><td>0.65</td><td>0.61</td><td>0.64</td><td>0.68</td><td>0.69</td><td>0.66</td><td>0.64</td><td>0.71</td><td>0.71</td><td>0.68</td></tr><tr><td>F1-score</td><td>0.71</td><td>0.61</td><td>0.70</td><td>0.65</td><td>0.60</td><td>0.69</td><td>0.42</td><td>0.66</td><td>0.64</td><td>0.54</td><td>0.72</td><td>0.61</td></tr></tbody></table>

ing our understanding and refining predictive models in the realm of cancer-related psychological outcomes.

Moreover, the model contributes to advancing post-cancer care by focusing on the specific needs of cancer survivors. By understanding the complex interplay of various factors contributing to FCR, healthcare providers can tailor interventions to address the unique challenges faced by each patient, thereby improving their quality of life and overall well-being. This is the first study, to our knowledge, using AI models to assess subjective, non-clinical variables from extensive healthcare consumption databases. It serves as a proof of concept that we can leverage in the future to address major and multifactorial issues such as recurrence itself.

The present study presents several limitations. First, our measure of FCR was based on a single-item indicator rather than a multi-item scale based on Patient Reported Outcomes (PRO) and therefore including risk of bias such as social desirability bias. Limited consensus exists on how to best measure FCR, but frequency/persistence of worries is considered a key feature of clinical FCR, and our indicator also already provided useful information [17,18,4]. Moreover, it used conventional ML models due to dataset size constraints, hindering the application of more sophisticated deep learning models. Also using up-to-date data in training the ML model would greatly enhance the accuracy of our findings and further advance the field. However, due to authorization constraints limiting data access to 2016, using more recent data, such as from 2023/2024, would be great. Additionally, the study solely focused on event counts without incorporating temporal information. As future research, we aim to address these limitations by integrating temporal

data and leveraging unlabeled reimbursement data to augment model performance.

By integrating temporal information into our analysis, we also aim to capture the dynamic nature of healthcare trajectories and better understand the evolving patterns associated with FCR in women with BC. This enhancement will enable us to develop more nuanced predictive models capable of capturing subtle changes in patient risk profiles over time. Incorporating temporal data into future research on FCR offers substantial benefits. Temporal data can enhance predictive accuracy by capturing the progression and evolution of FCR over time, revealing patterns and trends that static data might overlook. It enables a deeper understanding of how earlier events or treatments influence later FCR levels, identifies anomalies in patient anxiety patterns, and supports more personalized predictions. Advanced models like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) can help to analyze these temporal sequences to forecast future FCR trends.

To accomplish this, we will explore advanced deep learning techniques such as Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs) and Transformer-based models, which are well-suited for processing sequential data and capturing long-range dependencies. By applying these techniques to both labeled and unlabeled datasets, we aim to capture the time-dependent progression of healthcare events and enhance predictive performance. Using unlabeled data with semi-supervised or unsupervised learning techniques, such as autoencoders or clustering, could reveal hidden patterns and improve model generalizability.
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** (A) Summary chart of SHAP feature analysis and SHAP summary plot of feature importance for Experiment 2 settings using GB model, SFM and SMOTE. Here, the explanations are computed for the FCR class. SHAP values for classes 0 and 1 are symmetrical. This symmetry implies that if a feature contributes a certain amount towards class FCR, it equivalently diminishes the probability of being classified as class Non-FCR by the same amount. In the global explanation (left-side plot), all features are considered collectively to understand their overall impact on model predictions, with the bar plot function generates a global feature importance plot, where the global importance of each feature is calculated as the mean absolute value for that feature across all the provided samples. The localized analysis (right-side plot) offers a granular understanding of how each feature influences the model's decision-making process for a particular instance. Each instance is represented by a single dot on each feature row, illustrating the provided explanation. In this localized view, each feature's SHAP value is visually represented with a beeswarm plot, where positive contributions are displayed as points shifted towards the right side of the plot, indicating an increase in the predicted outcome. Negative contributions are shown as points shifted towards the left side of the plot, signifying a decrease. In this localized view, each feature is color-coded to denote its effect: high values are highlighted in red hues, while low values are marked in blue hues. (B) Predicted effect of SHAP feature analysis for single FCR observation. (C) Predicted effect of SHAP feature analysis for single Non-FCR observation. The features ATC_L4_N02AA (Natural alkaloids of opium), ATC_L4_M01AB (Acetic acid derivatives and related compounds), and BIO_L2_05_00 (Hematology) are also observed in single observations (B and C). (For interpretation of the colors in the figure(s), the reader is referred to the web version of this article.)</figcaption></figure>

## 5. Conclusions

The study aimed to develop a predictive ML model using healthcare reimbursement data to identify patients at risk of FCR in women with BC five years post-diagnosis. ML models showed promise in predicting

FCR, with the best-performing model achieving an AUC of 0.66 and a recall of 0.70. GB model consistently demonstrated high recall values, indicating its effectiveness in identifying at-risk patients. SHAP-based interpretation of the model results helped identify biological procedures, medical procedures, and drug consumption contributing most to the pre-
dictive model. Imaging procedures such as echography, spinal column radiography, and breast MRI, along with certain medications like those for acidity problems, benzodiazepines, broad-spectrum penicillins, and standard hormone assays, were associated with higher probabilities of FCR. Standard blood biochemistry were also found to be linked to higher probabilities of FCR.

This study presents a pioneering proof of concept for predicting FCR by leveraging healthcare reimbursement data. Grounded in the hypothesis linking FCR to anxiety and medical overconsumption, our research advances by using ML models to analyze non-clinical variables within extensive healthcare consumption data. Through the application of ML techniques, we illustrate how reimbursement data can accurately identify patients at heightened risk of FCR, marking a significant step towards addressing this multifaceted issue in cancer care. This progress not only enhances our comprehension of FCR using reimbursement data but also enables the implementation of population-wide screening programs spearheaded by national health insurance or facilitated through initiatives by other national public health institutes.

Future research directions suggested incorporating temporal information and leveraging unlabeled medicoadministrative data with semi-supervised learning and representation learning approaches to enhance predictive accuracy. The study underscores the significance of data-driven approaches in improving patient outcomes and advancing cancer care, particularly in identifying at-risk patients for timely interventions and personalized care strategies.

## Summary table

<table><caption>Table 2</caption><thead><tr><td>What is already known</td><td>What this study adds</td></tr></thead><tbody><tr><td><ul><li>FCR is a significant concern among BC survivors, impacting their quality of life and requiring effective management strategies.</li><li>Previous studies have highlighted the pervasive nature of FCR among cancer survivors but have primarily focused on psychological interventions rather than predictive modeling using healthcare reimbursement data. Also, association between FCR and medical overconsumption in early-stage BC survivors have been investigated.</li></ul></td><td><ul><li>This paper proposes a novel application of ML to predict FCR using healthcare reimbursement data, offering insights into long-term survivorship and paving the way for personalized interventions.</li><li>Our proposed approach enhances our understanding of the factors influencing FCR but also opens avenues for developing targeted interventions that can mitigate psychological distress in cancer survivors.</li><li>It enables the implementation of population-wide screening programs spearheaded by national health insurance or facilitated through initiatives by other national public health institutes.</li></ul></td></tr></tbody></table>

## CRediT authorship contribution statement

**Mamoudou Koume:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Lorène Seguin:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization. **Julien Mancini:** Writing – review & editing, Validation, Methodology, Formal analysis. **Marc-Karim Ben-diane:** Writing – review & editing, Validation, Methodology, Formal analysis. **Anne-Déborah Bouhnik:** Writing – review & editing, Supervision, Resources, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. **Raquel Urena:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This research is funded by the French League Against Cancer and Cancéropôle PACA (grant number: SHS-E-SP 2023). The VICAN survey is funded by the National Cancer Institute (Research and Development Contract No. 05-2011). Supports are gratefully acknowledged.

## Appendix A. Supplementary material

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.ijmedinf.2024.105705.

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