###### Abstract
Due to privacy policies, on certain occasions if a suitable anonymization of the data is not performed and/or the required authorizations to access some data are not obtained, the needed health studies cannot be carried out. Therefore, data availability should also be considered as a key factor [21]. * Another difference with other ML applications is the existence of different costs of failures; in the health area, the cost of a false negative (e.g., failing to detect that a patient has a specific disease) is usually much higher than the cost of a false positive (e.g., if a person is initially considered to have a disease that he/she does not really have, additional tests will be performed to rule this out, which may be costly but usually less harmful than failing to diagnose an existing disease). * It should also be considered that ML hardly ever follows a linear sequence ending at the first attempt; instead, it is rather an iterative feedback process where the stages interact with each other. Furthermore, in the field of healthcare, where the flow of data is continuous and constant, it is reasonable to assume that the model can be designed to be a \"learning\" model that must be continuously updated to improve its predictions over time. Therefore, the different stages needed to generate a reliable predictive model should be properly structured, from the acquisition of raw data to decision-making, which is essential to achieve the effectiveness of the model [22]. All this motivates the need to define the particularities of the application of ML techniques in the field of healthcare, where different stages in the ML workflow must be correctly defined and structured. The proper application of ML techniques would be beneficial for the clinicians, researchers, developers, and designers involved in the field of health, where the management of information acquires a transcendental role. It would favor the design of new products and services for improving healthcare access [23], creating truly accessible technological solutions [24], and enhance the relationship between health systems and people by providing adequate services at the right time [23]. Based on the above, the aims of this study are the following: (1) to show and develop the particularities of applying ML techniques in the field of healthcare, detailing all the stages that comprise this process, from the acquisition of raw data to the decision-making derived from the predictive model generated; and (2) to demonstrate and show its practical application in a real use case. Specifically, the ML process is applied in a cervical pain assessment study with patients affected by whiplash pathologies derived from traffic accidents or other causes. This case study shows the proposed methodology in action to solve a specific relevant problem. Moreover, the applied procedure can be used as an ML application guide for other similar studies in the field of healthcare. We believe that the combination of the use case study and the machine learning methodology is a relevant contribution of this paper. We do not remain in the theoretical/methodological part only or limit our work to apply different machine learning algorithms and compare the results, as many other works do; instead, we describe the whole machine learning process, highlighting the aspects that are more relevant for our use case but at the same time providing a general framework that could be used in other health-related projects. In this way, we think that the paper could be relevant both as a specific case study and also as a reference and guideline for other similar projects. The structure of the rest of this paper is as follows. In Section 2, we present the use case scenario studied and the clinical methods used for data collection. In Section 3, we describe the proposed procedure to develop predictive models for healthcare, illustrating each step with our work on the case study. In Section 4, we present an overall discussion of the proposal and the lessons learnt.