A Nobel Hybrid Approach for CHDES Model for Predicting Heart Disease Patients

In these days, the death of about one person per minute from heart disease is becoming a big concern. The relationship between men and women is often considered and may differ by area, and this ratio is including for 25-69-year-olds. This will not indicate that people of a different age group do not suffer from heart diseases. The prediction of heart disease (HD) occurrences in the medical field is an important task. Data collection allows predicting different diseases with further knowledge in the health service. A vast amount of patient-related details are held regularly. For predicting the occurrence of possible diseases, the stored data may help. In this paper, we have developed a new model of Expert System based on cardiovascular disease prediction named Coronary Heart Disease based Expert System (CHDES) in which we have used machine learning algorithms & health care data classifying individuals whether or not they have heart disease as per data records. This technology combines machine learning methods with an expert system that enables the physician and patients to anticipate risk and also diagnose heart attacks based on their symptoms. We have selected the 5 best features for the classification purpose with the help of the PCA dimensionality reduction method.