My bachelor thesis project: an analysis to predict cardiovascular diseases in obese patients
Machine learning is increasingly being integrated into the medical field, with applications ranging from preclinical data processing to bedside diagnostic support, from the analysis of radiographic images to the assessment of the risk of developing certain diseases. The latter plays an important role during the diagnostic process, through which physicians are able to determine the conditions affecting the patient under examination. A fundamental part of the healing pathway is the speed with which a correct diagnosis can be made.
The work presented is part of a project aimed at the development of a clinical decision support system (CDSS) capable of predicting the probability of the onset of certain comorbidities related to obesity. In particular, this thesis focuses on cardiovascular problems: starting from a dataset associated with patients and their respective diagnoses, a comparative analysis was carried out to identify the model that can most accurately predict the presence of these conditions.
Dataset credits: Kaggle
MIT