## 3 Proposed Procedure to Develop Predictive Models in Healthcare
### Stage 1: Clinical Target Definition  
In the first stage, the aim of the system is established--that is, the variables with clinical significance that the system should be able to predict are identified. Likewise, the final performance of the model and the statistical measures that will define its performance must also be defined. Measures such as the accuracy, precision, or recall are usual metrics used to assess the performance of a classification model, and metrics such as the Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE), to cite two examples can be used to evaluate the accuracy of a numeric prediction.  
In predictive classification in a healthcare domain, it is usual that some metric must be highlighted in such a way that the model must always be generated with the aim to minimize it. This metric is usually the number of false negatives (affecting the recall metric). The reason for this is that, in healthcare, false negatives and false positives have no similar costs, which has always been an issue that clinicians have had to deal with [39]. Moreover, the clinical need must be identified (e.g., to classify a certain type of pathology, to predict a pattern or behavior, etc.). In addition, the sample size and viability of the project must be assessed prior to its realization [34].  
**Application to our Case Study**  
In our case study, the aim of the prediction model is to predict the presence of cervical pain (the target variable) in patients who have suffered whiplash or suffer from chronic cervical pathology. In our collected dataset, the cervical pain is a binary variable (the presence or absence of pain) which has been reported by the collaborating subjects, who were real patients undergoing an assessment process in a hospital.  
Predicting the presence of cervical pain is of interest especially in the forensic field, as the incidence and prognosis of whiplash injury from motor vehicles is relevant to insurance litigations for pain and suffering [29]. The aim is to determine the presence or absence of pain with enough confidence to be able to aid clinicians to detect possible magnifications of the injury by the affected individuals and thus establish an unbiased compensation for the cervical pain [40]. It can help to identify and objectivity pain in patients with a high degree of anxiety and in hypochondriac patients.  
Without a loss of generality, the following target metrics have been determined for the purpose of this study, whose required threshold values have been stablished according to the criteria of clinical experts (for this particular case study, they considered that achieving this quality criteria would be enough for the system to be used as a decision-support system in production):  
* Accuracy: greater than 85%.
* Precision: greater than 85%.
* Recall: greater than 90%.  
The sample size is 302; although this is not a very large dataset, it contains a lot of variables with relevant information to characterize the presence of cervical pain during insurance litigation, which allows predicting the target variable, thus considering the project viable.