Developing a Predictive Model of Stroke using Support Vector Machine

摘要

Health is a fundamental human right of all the Filipinos in the Philippines, as stated by the Philippine Constitution of 1987. Based on the data published by the World Health Organization in 2018, there are 41 million deaths occurred because of stroke and its complications. Thus, given the parameters for the variables of risk factors of stroke, a predictive model is developed for the occurrence of stroke based on the medical records of the patient. To ensure quality data, the medical data of the patients underwent data pre- processing, principal component analysis is used for dimension reduction. The model is evaluated using accuracy, precision, recall, F1 score, and area under curve. The study used datasets of 1500 patients from Cavite, Philippines. This study used 60 percent for training the model, and 30 percent is used for testing the model and 10 percent for validating the model. The SVM model achieved an accuracy of 99% for training the data, 98.89% for testing, and 97.33% for validation. The results of the model show the potential use of the predictive model for stroke, thus, remains relevant for researchers and practitioners in the medical and health sciences field.