Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction - El-Ibrahimi, Abdeljalil; Terrada, Oumaima; El Gannour, Oussama; Cherradi, Bouchaib; El Abbassi, Ahmed; Bouattane, Omar (International Journal of Online and Biomedical Engineering (iJOE), 2023)
According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.