Heart Disease Detection using Machine Learning Technique

Heart disease (HD) is a serious health problem which may affect large numbers of people across the world. As a result, detecting a heart condition at an early stage will be beneficial to treatment. The number of persons with heart disease is rapidly increasing, necessitating the development of a system that can detect heart disease more easily. The presence or absence of disorder is determined by the patient's smoking status. The cardiac disease system can identify patients who are high-risk and define the most important variables in cardiovascular patients but also build a model so that they can distinguish between them easily and understandably. The Machine Learning algorithms are applied and compared based on the characteristics like age, Chest ache, Blood Pressure (BP), sex, cholesterol and heartbeat. The main focus of this paper is to develop a basic machine learning model to enhance the diagnosis of the heart condition in the right manner. The different techniques such as Logistic Regression, K-Nearest Neighbor (K-NN), Decision Tree, Naive Bayes, Random Forest and Support Vector Machine are applied for machine learning and achieved the better results in this work.