###### Abstract  
Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources. data mining; data anonymization; health; cervical injury; neck pain; inertial sensors +
Footnote †: journal: Medical  
techniques that allow computers to learn from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets\" [8]. Consequently, ML is generating growing interest in the field of healthcare (e.g., see [9; 10]) for relevant special issues related to this topic), mainly derived from its possible applications, such as assessing the effectiveness of treatments, detecting fraud and abuse in health insurance, managing healthcare, making lower-cost medical solutions available to the patients, detecting symptoms and diseases [11], discovering treatment patterns from electronic medical records [12], detecting groups of incidents [13], and identifying medical treatment methods [2; 3]. Likewise, ML also presents health benefits: (1) a potential reduction in the time and effort required for diagnosis and treatment, (2) the ability to examine multiple areas simultaneously, (3) a decreased potential for human error, and (4) data that are accessible anytime and anywhere [14]. Besides, DM and ML are key in the path towards personalized medicine, where the goal is to customize treatments to the specifics of each individual [15; 16; 17; 18]. However, to take full advantage of the benefits offered by ML in the field of healthcare, several considerations are necessary. Among others, the following aspects can be highlighted:  
* The data have to be structured and organized in order to properly process and transform them into suitable variables, which is essential in the development of any pattern recognition software and a highly problem-dependent task [19; 20]. * Moreover, the secure treatment and management of data acquires special relevance in the field of healthcare, where the privacy of the patient must be ensured. The management of sensitive data contrasts with other fields of application of ML (fraud detection, stock prediction, etc. ), where anonymization treatments may be sometimes not necessary or critical. Therefore, a specific treatment involving the anonymization and categorization of the data must be performed in order to ensure the privacy of the patients [21].