A Machine Learning-based approach for Classification of Heart Disease in Health Care Domain through Data Mining
Diagnosis of heart disease is a complex undertaking requiring a great deal of information and experience. Heart illness is traditionally predicted through medical examinations or a range of medical procedures such as ECG, stress test and MRI, etc. Heart illness. Today, there is an extensive amount of data about healing that includes secret information in the healthcare industry. This knowledge is useful for efficient decision-making. Computer information, in conjunction with cutting-edge data mining techniques, is utilized to get relevant results. A neural network is often used to predict heart disease diagnosis. A Neural Network is used in this study to build a Heart Disease Prediction System (HDPS). The HDPS system predicts the patient's risk of heart disease. The system uses gender, blood pressure, cholesterol, and 13 medical indicators to do this. Obesity and smoking are introduced as new criterion for greater accuracy. Results showed that the neural network predicts cardiac illness with an 99.08% accuracy.