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# Frontotemporal Dementia Detection Model Based on Explainable Machine Learning Approach

Km Poonam, Rajlakshmi Guha, Partha P. Chakrabarti

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Km Poonam, Rajlakshmi Guha, Partha P. Chakrabarti. Frontotemporal Dementia Detection Model Based on Explainable Machine Learning Approach. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.230-242, 10.1007/978-3-031-38296-3_18 . hal-05466037

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# Frontotemporal Dementia Detection Model Based on Explainable Machine Learning Approach

Km Poonam✉️, Rajlakshmi Guha✉️, and Partha P. Chakrabarti✉️

IIT, Kharagpur, Kharagpur 721302, West Bengal, India
poonamk@iitkgp.ac.in, rajg@cet.iitkgp.ac.in, ppchak@cse.iitkgp.ac.in

**Abstract.** Early diagnosis of neurodegenerative disorders is one of mainstream neuroscience's most significant challenges. Although there is no cure for this group of disorders, the importance of medical treatment primarily depends on when the patient is diagnosed. One of the devastating neurodegenerative diseases is Frontotemporal Dementia (FTD). The primary FTD spectrum disorders include Behavioral Variant FTD (BV), Non-fluent Variant Primary Progressive Aphasia (PNFA), and Semantic Variant (SV). FTD detection has been thoroughly researched. However, research findings typically have less impact on clinical practice for the following reasons: (1) FTD subtypes detection has not been studied substantially; (2) most research relies heavily on a single diagnostic modality, particularly neuroimaging; and (3) current research focuses primarily on improving the accuracy of machine learning and deep learning models whereas neglecting their interpretability. So, Clinicians find it difficult to interpret these complex models. In this study, to assist medical specialists with supporting diagnostic tools, we investigate how well a machine learning algorithm performs in developing an accurate FTD subtype. The dataset consists of neuropsychiatric inventory data of 288 subjects from the frontotemporal lobar degeneration neuroimaging initiative (NIFD) database. Experiments are carried out by investigating different machine-learning classification algorithms and providing various explainers for the models' global interpretability via Shapley Additive Explanations (SHAP) and local interpretability via Local Interpretable Model-agnostic Explanations (LIME). Obtained results show high accuracy in detecting FTD subtypes when Random Forest and Extreme Gradient Boosting techniques achieve a test accuracy of 85.05% and 86.21%, respectively.

**Keywords:** Frontotemporal Dementia · Machine Learning · Explainable Artificial Intelligence · Shapley Additive Explanations · Local Interpretable Model-agnostic Explanations
Abbreviation

CDR\_LANG: Clinical Dementia Rating: Language subscore

CDR\_BEHAV: Clinical Dementia Rating: Behavior subscore

CDR\_TOT: Clinical Dementia Rating: Total score

CDR\_BOX\_SCORE: Clinical Dementia Rating: Box Score

MMSĒ\_TOT: Mini-Mental State Exam: Total score

TRCOTOT: California Verbal Learning Test: Total correctly recalled items over four learning trials

CORR30: California Verbal Learning Test: Correctly recalled items after 30 s delay

INTR30: California Verbal Learning Test: Intrusion in recall after 30 s delay

CORR10: California Verbal Learning Test: Correctly recalled items after 10 min delay

INTR10: California Verbal Learning Test: Intrusion in recall after 10 s delay

RECOG: California Verbal Learning Test: Correctly recognized items

MTTTIME: Modified Trails: Completion time, in seconds

MTERROR: Modified Trails: Errors

DIGITFW: Digit Span: Maximum forward recall span

DIGITBW: Digit Span: Maximum backward recall span

DCORR: Verbal Fluency - D Words: Correct D words generated

ANCORR: Verbal Fluency - Animals: Correct animals generated

BNTCORR: Boston Naming Test (15-item): BNT Total

MODREY: Montreal Cognitive Assessment

REY10M: Modified Benson Figure

PPVTANI: Peabody Picture Vocabulary Test: Animate

GDESTOT: Geriatric Depression Scale

FAQ\_BILLS: Functional Activities Questionnaire: Bills

FAQ\_TAXES: Functional Activities Questionnaire: Taxes.

# 1 Introduction

Frontotemporal dementia (FTD) is caused by the degeneration of the neurons in the frontal and temporal regions of the brain. These brain regions control essential cognitive functions: social cognition, language, emotion, memory, and decision-making abilities [1,2]. Frontotemporal dementia has primarily three clinical subtypes, namely, behavioral variant frontotemporal dementia (BV), nonfluent variant primary progressive aphasia (PNFA), and semantic variant primary progressive aphasia (SV) [3,4]. FTD is one of the commonest forms of young-onset dementia. That's why it is generally assumed that an efficient treatment method should approach subjects at risk of FTD early in the disease's progress [5]. There is no single test for FTD diagnosis. Clinicians investigate disease symptoms and signs while trying to rule out other possible causes. Early diagnosis of FTD can be difficult because FTD symptoms frequently overlap with other disorders. Usually, clinicians extensively test memory and reasoning
skills using cognitive tests or neuropsychiatric inventory. Here in this work, we took cognitive tests and neuropsychiatric inventory for the detection.

In this paper, we presented a machine-learning (ML) approach to forecast the individual's clinical status. To demonstrate the interpretability of the ML model, we also presented local and global explainers. The majority of prior research concentrated on a static variation of the problem to predict the clinical diagnosis sometime in the future [6-9]. All previous studies emphasized enhancing system performance while ignoring explainability challenges. These studies made enormous breakthroughs in prediction. However, these works are still not expected to be admissible in the clinical setting. The usefulness of academic research findings and their use in clinical context differs significantly [7]. Subsequently, even after their high-performance metrics, clinicians do not usually rely on the most recent technical methods, such as ML and DL, because model performance and interpretability appear to be in conflict. It is essential to have a balanced performance-interpretability trade-off [10]. The majority of these methods are by default confusing and unable to explain why did it come to a particular conclusion and what is it medical relevance [11]. Complex ML algorithms are used to extract patterns from datasets, however these patterns may not always represent accurate. As a result, medical specialists are suspicious of findings given by black-box models that lack detailed and simple explanations [12]. Due to these constraints, ML methods used in the medical field typically do not think about complex models and conversely rely on more understandable and straightforward models at the cost of accuracy [13]. Recent research has attempted to shed light on the workings of complicated models to explain their predictions, either by figuring out how the models function or by interpreting the choices they make [14]. This emerging approach is known as explainable Artificial Intelligence (XAI), which is the ability of AI methods to explain their outcomes in human-comprehensible terms. We incorporated this approach and contributed to this study in the following ways:

- We constructed ML tree-based ensemble classifiers based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to predict FTD subtypes utilizing the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset.

- We show how to keep interpretability even while using complicated ensemble models (RF and XGBoost) are used. With this strategy, two objectives are intended to be achieved: (1) To demonstrate how a computational framework was developed and validated for detecting FTD patients, and (2) to show how to generate comprehensive explanations for ML results using global interpretability via Shapley Additive Explanations (SHAP) values and local interpretability via Local Interpretable Model-agnostic Explanations (LIME).

- We selected the minimal set of features based on preprocessing step (heatmap correlogram) and SHAP summary plots.
# 2 Material and Methods

## 2.1 Dataset

The dataset used in this study was retrieved from Alzheimer's disease neuroimaging initiative (ADNI) study project, namely, the frontotemporal lobar degeneration neuroimaging initiative (NIFD). The data access committee approved access to the NIFD data. The data set contains 288 subjects for baseline and 918 entries for longitudinal analysis for 123 attributes. Medical experts labeled this data into four different diagnostic labels: Cognitively Normal (CN): 134, Behavioral Variant (BV): 75, Progressive Non-fluent Aphasia (PNFA): 40, and Semantic Variant (SV): 39. Table 1 presents descriptive statistics of baseline features.

<table><caption>Table 1. Descriptive statistics from the dataset</caption><thead><tr><th></th><th>CN (n = 134)</th><th>BV (n = 75)</th><th>PFNA (n = 40)</th><th>SV (n = 39)</th><th>Combined (n = 288)</th></tr></thead><tbody><tr><td>Education (years)</td><td>20.97 ± 17.06</td><td>17.85 ± 13.81</td><td>18.07 ± 13.34</td><td>18.38 ± 13.51</td><td>19.40 ± 15.31</td></tr><tr><td>CDR LANG</td><td>0.785 ± 2.40</td><td>0.691 ± 0.55</td><td>1.358 ± 0.68</td><td>0.782 ± 1.54</td><td>0.258 ± 1.84</td></tr><tr><td>CDR_BEHAV</td><td>0.774 ± 2.40</td><td>1.328 ± 0.79</td><td>0.410 ± 0.37</td><td>0.846 ± 1.58</td><td>0.311 ± 1.89</td></tr><tr><td>CDR_TOT</td><td>0.021 ± 0.10</td><td>1.075 ± 0.56</td><td>0.337 ± 0.94</td><td>0.653 ± 0.30</td><td>0.491 ± 0.67</td></tr><tr><td>CDR_SB</td><td>0.038 ± 0.15</td><td>6.006 ± 3.15</td><td>2.050 ± 2.29</td><td>3.615 ± 1.95</td><td>2.736 ± 3.25</td></tr><tr><td>MMSE_TOT</td><td>29.38 ± 0.76</td><td>23.71 ± 5.62</td><td>23.743 ± 8.05</td><td>22.66 ± 9.34</td><td>26.23 ± 6.13</td></tr><tr><td>TRCOTOT</td><td>10.83 ± 17.3</td><td>18.71 ± 9.12</td><td>19.00 ± 10.7</td><td>15.15 ± 9.24</td><td>14.57 ± 14.21</td></tr><tr><td>CORR10</td><td>1.835 ± 5.57</td><td>3.270 ± 3.01</td><td>4.736 ± 3.39</td><td>1.486 ± 2.68</td><td>2.554 ± 4.54</td></tr><tr><td>RECOG</td><td>1.962 ± 5.93</td><td>6.310 ± 3.44</td><td>7.078 ± 3.15</td><td>5.567 ± 3.58</td><td>4.257 ± 5.24</td></tr><tr><td>MTTTIME</td><td>27.73 ± 13.8</td><td>61.64 ± 46.3</td><td>61.68 ± 40.4</td><td>44.56 ± 30.5</td><td>43.36 ± 35.07</td></tr><tr><td>MTCORR</td><td>13.73 ± 1.74</td><td>8.797 ± 6.00</td><td>11.31 ± 4.89</td><td>12.05 ± 5.18</td><td>11.89 ± 4.63</td></tr><tr><td>DIGITFW</td><td>5.208 ± 5.33</td><td>5.283 ± 2.29</td><td>4.342 ± 2.04</td><td>5.864 ± 3.44</td><td>5.197 ± 4.12</td></tr><tr><td>DCORR</td><td>15.00 ± 6.16</td><td>6.108 ± 5.20</td><td>5.842 ± 5.53</td><td>7.783 ± 5.28</td><td>10.50 ± 7.13</td></tr><tr><td>ANCORR</td><td>24.03 ± 5.52</td><td>9.09 ± 6.76</td><td>10.10 ± 8.24</td><td>7.860 ± 5.00</td><td>16.14 ± 9.74</td></tr><tr><td>BNTCORR</td><td>14.45 ± 0.81</td><td>11.43 ± 4.22</td><td>11.86 ± 3.93</td><td>5.000 ± 3.98</td><td>12.08 ± 4.28</td></tr><tr><td>PPVTANI</td><td>2.831 ± 2.80</td><td>2.903 ± 1.62</td><td>3.411 ± 1.13</td><td>1.250 ± 1.93</td><td>2.692 ± 2.24</td></tr><tr><td>GDSTOT</td><td>3.000 ± 7.35</td><td>5.081 ± 4.75</td><td>4.435 ± 6.15</td><td>7.210 ± 7.50</td><td>4.298 ± 6.24</td></tr><tr><td>NPI_APPSEV</td><td>-5.475 ± 1.85</td><td>0.953 ± 3.03</td><td>-3.781 ± 3.34</td><td>-0.54 ± 3.54</td><td>-2.43 ± 3.95</td></tr><tr><td>FAQ_TAXES</td><td>0.338 ± 1.62</td><td>2.500 ± 1.16</td><td>1.000 ± 1.04</td><td>1.264 ± 1.08</td><td>1.197 ± 1.57</td></tr><tr><td>FAQ Events</td><td>0.00 ± 0.00</td><td>1.300 ± 0.83</td><td>0.406 ± 0.66</td><td>0.941 ± 0.73</td><td>0.588 ± 0.80</td></tr></tbody></table>

## 2.2 Data Preprocessing

A sequence of methods has been applied for managing missing data, feature selection, and correlation analysis for effective dataset preprocessing. The dataset contained a significant amount of missing data, as seen in Table 1. We examined preprocessing strategies (mean, mode, and median) to handle the missing data. The mean imputation technique was used to handle missing data. We used a heat map of features to perform correlation analysis. We can observe from
<figure><img src="image_4.png" /><figcaption>Fig. 1. Heatmap correlogram to visualize the correlation among features</figcaption></figure>

Fig. 1 CORR10 and TRCOTOT were highly correlated (0.90) features, and since CORR10 had higher feature importance than TRCOTOT, TRCOTOT was eliminated. The dataset set was divided as 20% data used for validation and 20% data for testing, and 60% data used for model training. The models' performance was evaluated using the test set. We can observe that the dataset is imbalanced; as a result, the synthetic minority oversampling method (SMOTE) is employed to deal with the class imbalance in the training set by generating synthetic samples for the minority class. The SMOTE algorithm helps to overcome the over-fitting problem posed by random oversampling. The feature selection step is assimilated based on SHAP summary plot and heatmap correlogram.

## 2.3 Proposed Methodology

The problem setup is given the subjects' neuropsychiatric test scores and clinical labels for baseline; we intend to predict the subjects' clinical status. The proposed system consists of primarily two tasks: to classify the subjects into one of the diagnostic labels CN, BV, PNFA, and SV and to show how to generate comprehensive explanations for ML results using global and local interpretability approaches. The suggested framework's development process consists of various steps, as presented in Fig. 2.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Proposed framework for the development process. SMOTE: Synthetic minority oversampling method, SHAP: Shapley additive explanations, LIME: Local interpretable model-agnostic explanations</figcaption></figure>

We considered five baseline classifiers: K-nearest Neighbor (KNN), RF, Multi-layer Perceptron (MLP), XGBoost, and Support Vector Machine (SVM). The RF and XGBoost classifiers are employed because they are ensemble methods and feasible for explaining the diagnostic decisions. Though SVM and MLP have a wide range of applications for fitting highly non-linear models to data and achieving excellent results, the resulting models are vague, making it challenging to comprehend their decisions [10]. As a result, we decided to classify individuals using the RF and XGBoost model. After creating the XGBoost model, we implemented two explainers, SHAP and LIME to interpret the model's prediction.

**Global Interpretability:** We have used TreeSHAP plots to interpret the RF and XGBoost models on a global level. Using the XGBoost model, Fig. 3 shows how much each feature influences individual groups. We can explain a prediction in the context of a game where the payoff is the prediction, presuming that each instance's feature value is a player. A strategy from coalitional game theory is called Shapley values which is the average marginal contribution of a feature value across all possible coalitions. To calculate the Shapley value of $j^{th}$ feature, we have to evaluate all possible sets of feature value with or without the $j^{th}$
feature. If we add a large number of features, finding the exact solution will be
very problematic because the number of possible coalitions will exponentially
increase. In the study [15], researchers propose an approximation method with
Monte-Carlo sampling to compute the SHAP value for individual features. Those
features which have high absolute Shapley values are important in the prediction.
Since we're primarily concerned with the overall significance, we average the
absolute Shapley values for each feature throughout the dataset. Shapley value
for jth feature is:

$$
S_j = \frac{1}{n} \sum_{i=n}^{n} |\Phi_j^i| \qquad (1)
$$

$$
|\Phi_j| = \hat{f}(x_{+j}^n) - \hat{f}(x_{-j}^n) \qquad (2)
$$

where $\hat{f}(x_{+j}^n)$ the prediction for x with including j is a feature, $\hat{f}(x_{-j}^n)$ is the prediction for x without including j feature, n is the number of instances, and $|\Phi_j^i|$ is the marginal contribution.

**Local Interpretability:** It is conceivable to illustrate graphically how features influence a subject's labeling with a specific class by isolating a single subject. We used the LIME method to explain any particular instance's prediction. LIME is a Python package that generates a valid local interpretation to resolve the interpretability problem of the complex model. To reach a particular outcome, we can enlarge a data point in a model and get a detailed look at which features significantly impacted the model. The approach attempts to comprehend the model by varying the input of data points and analyzing how the predictions vary. LIME tweaks the feature values for a single data sample and analyzes the effect on the output. As a result, LIME provides a set of explanations indicating the contribution of every feature for predicting a particular instance. This typically provides local interpretability and helps us identify which feature changes will impact the prediction most.

3 Results and Discussion

3.1 Classification Performance

The Table 2 illustrates the test performance of the baseline classifiers (RF, XGBoost). We also experimented with other classifiers (SVM, KNN, and GNB), but RF and XGBoost performed well. The models' performance has been assessed using precision, recall, accuracy, and F1-score measures. We can observe that the RF and XGBoost model have a multiclass classification accuracy of 85.05% and 86.21%, respectively. Grid search with 10-fold cross-validation was applied to determine the most appropriate hyperparameters: number of estimators = 100, maximum depth = 7, minimum samples split = 4, maximum features = 19, learning rate = 0.1.
<table><caption>**Table 2.** Classification performance. RF: Random forest, XGB: Extreme gradient boosting, SVM: Support Vector machine, KNN: K Nearest Neighbor, GNB: Gaussian naive bayes, CN: Cognitively normal, BV: Behavioral variant, PNFA: Progressive non-fluent aphasia, SV: Semantic variant</caption><thead><tr><th rowspan="2">Diagnostic Labels</th><th colspan="2">Precision</th><th colspan="2">Recall</th><th colspan="2">F-1 Score</th><th colspan="5">Accuracy (%)</th></tr><tr><th>RF</th><th>XGB</th><th>RF</th><th>XGB</th><th>RF</th><th>XGB</th><th>RF</th><th>XGB</th><th>KNN</th><th>SVM</th><th>GNB</th></tr></thead><tbody><tr><td>CN</td><td>0.96</td><td>0.96</td><td>1.00</td><td>0.98</td><td>0.98</td><td>0.97</td><td>85.05</td><td>86.21</td><td>66.67</td><td>78.16</td><td>80.46</td></tr><tr><td>BV</td><td>0.68</td><td>0.81</td><td>0.76</td><td>0.76</td><td>0.72</td><td>0.79</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PFNA</td><td>0.64</td><td>0.64</td><td>0.58</td><td>0.75</td><td>0.61</td><td>0.69</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SV</td><td>0.91</td><td>0.83</td><td>0.71</td><td>0.71</td><td>0.80</td><td>0.77</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

## 3.2 Models Explainability

We presented the explainer methods to keep interpretability even while using complex ensemble models (RF and XGBoost). Global explanations can be generated by combining the Shapley values. If we run the SHAP algorithm for a data point, a matrix with Shapley values is generated. This matrix includes one row for each data point and one column for each feature. By examining the Shapley values of the matrix, we were able to interpret the whole model. The SHAP method was implemented using shap Python package. In Python's scikit-learn machine learning toolkit, this approach works for tree-based models like XGBoost. We evaluated the feature contributions of the XGBoost model based on SHAP explainers. Figure 3 displays feature importance for each class. The most influential feature is CDR\_LANG, followed by CDR\_BOX\_SCORE, and the lowest feature is PPVTANI from the neuropsychiatric inventory. The global feature importance provides a broad overview of how each feature performs, but the direction in which these effects will manifest needs to be clarified. For instance, we cannot predict if a high CDRSB rating will enhance the chances the CN, BV, PNFA, or SV class will be chosen.

We can examine the classifier's functionality by observing each feature's different values using the SHAP summary plots shown in Fig. 4. Each dot indicates the effect of a specific feature on a specific class for a given instance, and it is colored based on how much the value contributes to the model's influence. Red indicates a high feature value, and blue indicates a low one. We observe that each class is in a different order. For CN and PFNA classes, CDR\_LANG is of higher importance than CDR-SB, but CDR-SB has the highest impact on the BV class. The model displays a high extent of nonlinearity due to the effects of multiple features being spread out over comparatively wide ranges. We observe that high CDRSB values significantly positively influence the model for forecasting the BV class, indicating that CDR\_BOX\_SCORE is a feature that enhances the chance of semantic variants. Low values of CDR\_LANG, CDR\_BOX\_SCORE, and CDR\_TOT have a significant favorable influence on the model for the CN class prediction. On the contrary, high MMSE TOT, ANCORR, and DISITFW values have a significantly positive effect on predicting the CN class. It can be observed that the BV class has low values of CDR\_LANG and CDR\_BOX\_SCORE,
which leads to a substantial negative effect on the model. The NPI_APASEV, and NPI_MOT features are critical for the BV class. A high value of BNTCORR and low values of CDR_TOT and CDRhti SCORE negatively affect the model for predicting the SV label. Only higher values of DCORR and DISITFW positively impact the model for SV prediction. Furthermore, we can determine outliers' impact by plotting a feature's influence on an individual instance. We used the LIME method for explaining any particular instance's prediction illustrated in Fig. 5. LIME trains local models to interpret individual predictions contrary to a global model. LIME module tries to solve the problem of model interpretability by generating locally valid interpretations. In the figure, the plot for one subject belongs to the BV diagnosis. Most features have a strong positive impact, which explains why the subject's likelihood of being identified as SV is high. This explanation can be utilized to assess the patterns of falsely predicted subjects. Specifically, we examine the impact of features on the predicted and actual classes of randomly chosen subjects.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Global feature contribution of XGBoost model in diagnosis. XGBoost: Extreme gradient boosting</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** SHAP summary plots of each diagnosis using XGBoost. SHAP: Shapley Additive Explanations, XGBoost: Extreme gradient boosting</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Local interpretability of XGBoost model for an instance. LIME: Local interpretable model-agnostic explanations, XGBoost: Extreme gradient boosting</figcaption></figure>
# 4 Conclusion

FTD subtypes classification using neuropsychiatric tests is an effective approach that can help clinicians provide early detection. In this study, We evaluated how well XGBoost and RF performed to make an accurate FTD diagnosis. Classifiers used the baseline dataset to classify the subjects as CN, BV, PNFA, and SV. The predictions were analyzed on various measures, and prediction explanations were provided using global (SHAP) and local(LIME) explainability methods. There is no previous study for comparison, so best of our knowledge; this is the first work to explain FTD subtype prediction model explainability, showing both high accuracy and interoperability. Notably, despite the relatively small dataset size, the results exhibited significant consistency among the tested approaches, confirming their dependability. Of course, increasing the original size of the dataset may result in improved overall test accuracy and encourage the development of Deep Learning algorithmic strategies.

**Acknowledgements.** The dataset used in the study obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) study's project namely, Frontotemporal Lobar Degeneration Neuroimaging Initiative (NIFD). We are thankful to the data access committee for approving the access of NIFD data.

**Competing Interests.** The authors report no competing interests.

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