The Application of Machine Learning in Predicting The Risk of Heart Disease With Decision Tree Algorithm - Wahyudi, Dicky; Ayuningsih, Ekatri (Cattleya Darmaya Fortuna, 2024)
This research designs a web-based application that utilizes Machine Learning technology to predict the risk of possible heart disease with algorithms. This research aims to develop a web-based application that uses Machine Learning technology with the Decision Tree algorithm to predict the risk of heart disease. The research process begins with the collection of relevant medical data used in the training of Machine Learning models. Next, the web application is integrated with IBM Cloud services such as Watson Studio and IBM Cloud Object Storage to store user data and access configured Machine Learning algorithms for heart disease prediction. The data is processed and auto-configured using Machine Learning services from IBM Cloud, then the Machine Learning model is trained with Decision Tree algorithm using the processed dataset. As a result, a web application was successfully developed and integrated with IBM Cloud services, capable of providing heart disease prediction with sufficient accuracy. The application's simple and user-friendly interface allows users to easily access the heart disease prediction service without requiring in-depth technical knowledge. Thus, this research successfully created a solution that can assist in the early diagnosis of heart disease, provide direct benefits to the community, and expand access to heart health information widely. This application is built using the Flask framework and integrated with Machine Learning services from IBM Cloud. The use of the Decision Tree algorithm with an accuracy of 91.9% is based on a dataset that is processed and configured automatically using Machine Learning services from IBM Cloud.