Abstract
Heart disease diagnosis is a very hard task in the medical field, so the mortality rate is increased every day. Also, the diagnosing process is implemented in recent times to predict heart disease. The method of diagnosing a disease in the medical field can be regarded not only as a new unknown situation to obtain clinical data and data collected from clinical experience, but also as a decision-making process as well as a doctor's diagnosis. The detection of heart abnormalities mainly depends on the examination of the ECG signal at the appropriate sampling period. The data is trained and tested must include more data to get the data as features. These properties are an accurate measure of the diagnosis of heart disease. The conventional system is having some problems like processing time is high, and it gives low accuracy, so the proposed Regressive Learning-Based Neural Network Classifier (RLNNC) system is implemented. The proposed system RLNNC presents a fully automated algorithm for the classification of heart disease, based on the Regressive Learning-Based Neural Network Classifier (RLNNC) and automated initial seed detection. With the advancement of machine learning and information technology, the development of an automated system. This can be predicted the same on this basis for patients with heart disease, and the drug occurs for the benefit of detecting and analyzing the heart disease. Analysis has shown that the proposed Regressive Learning-Based Neural Network Classifier (RLNNC) based techniques promote greater efficiency and higher accuracy than traditional methods.