A Review on Predictive Model for Heart Disease using Wearable Devices Datasets - Mohd Syafiq Asyraf, Suhaimi; Nor Azuana, Ramli; Noryanti, Muhammad (Penerbit UniMAP, 2024)
Heart diseases were the number one killer in Malaysia based on the data from the Department of Statistics Malaysia in the previous year. Heart diseases were the principal causes of death for the population aged 41 and above. Many studies have discovered the factors that cause heart disease and ways to prevent it. Among the ways to prevent heart disease include analysis of the patient’s historical data, developing predictive modeling involving statistical and machine learning techniques, and monitoring health conditions through wearable devices. This paper reviewed the predictive model applied in heart disease prediction using wearable device datasets. Artificial neural networks (ANNs) have grown in popularity in data mining and machine learning for their ability to classify input data into several categories by detecting hidden connections in the data, which is beneficial in predicting correct classifications. Other approaches, such as Naive Bayes, Support Vector Machine, and Decision Tree algorithms, are used to analyze medical data sets to forecast cardiac disease. According to the survey, ANNs are likely to be good for heart disease prediction in terms of classification accuracy on training and test datasets.