Revolutioning Healthcare: A Superficial Learning Approach to Predict Heart Diseases by Using Artificial Intelligence (AI) Logic

The prevalence of cardiovascular illnesses is on the rise, making early disease prediction all the more crucial and worrisome. Making this diagnosis is no easy feat; it requires speed and accuracy. The primary objective of the study article is to identify the patient subset most at risk for cardiovascular disease using a battery of medical diagnostic criteria. By analyzing the patient's medical history, we developed a technique to forecast the likelihood of a heart disease diagnosis. Clinical decision support systems have made extensive use of AI approaches for the accurate prediction and diagnosis of a wide range of disorders. Here, we present Superficial Neural Health Learning (SNHL), an AI-based method for accurate heart disease prediction, and we cross-validate it with K-Nearest Neighbor (KNN), a traditional learning method, to see how well it works. Many of the observable risk factors are shared by heart disease patients and can be utilized to make a good diagnosis. A system that takes these risk factors into account might be useful for both doctors and patients; it could alert them to the possibility of heart disease before they incur the expenses associated with unnecessary hospital visits or expensive checkups. In order to diagnose and categorize the patient with cardiac illness, we employed various deep learning algorithms, including KNN and SNHL. In order to control the model's applicability and enhance the accuracy of heart attack prediction in any person, a very helpful technique was applied. When compared to earlier classifiers like naïve bayes, etc., the suggested model's strength in predicting if a person has heart disease using KNN and SNHL was rather satisfying. Applying the provided model to determine the classifier's likelihood of correctly and precisely identifying the heart condition has thus relieved a considerable amount of strain. Both the quality and affordability of medical treatment are improved by the given heart disease prediction system and it is easy to learn a lot about which people are likely to have heart disease from this method.