Overview

Heart attack is one of the most serious problems of the healthcare industry in India. While it hasbeen known that Indians are prone to heart attacks a decade earlier than their westerncounterparts, experts have expressed concerns over lowering of patient's age. Today's fast pacedlifestyle has brought in a lot of negative impacts on the mind, body and heart. Irregular sleepingpatterns, bad eating habits, skipped meals lead to harmful diseases. Also the prevalence of heartattacks amongst younger lot is on a steady rise. Data has also revealed that 48 percent of patientshospitalized for cardiac emergencies in 2018 were less than 50 years of age. The awarenessabout the heart failure is very low in our country. Most people do not know the differencebetween a heart attack and heart failure. In this experiment, we used Machine Learningapproaches to identify whether a person (Elders and Teenagers) has heart disease or not (and thencompared them). The teenager data is collected through a survey done using Google form and thedata of person whose age is greater than 40 is used from UCI research site for study purpose, which is refined and cleaned and is then processed using various Soft Computing Techniques .The results thus produced are then analyzed and a plausible prediction is made regarding theaccuracy in diagnosing and predicting heart disease. Here we are using KNN, tree, SVM, Random forest, Neural Network, Naïve Bayes, CN2, XGBoost, SMOTE and Adaboost as oursoft computing method, which is combination of fuzzy logic and artificial neural network. It iscalled Adaptive because it is a closed loop system. This research study is an attempt to reducethe efforts and time put in by the doctor by automating the risk prediction with the help of a SoftComputing technique. The resulting system will contribute more effectiveness and will givemore than 90% accuracy to predict Heart diseases.The soft computing tools are today's need in health care application, which can perform dataanalysis and modeling, and they can assist the physician to make right and prompt clinicaldecisions. Extracting patterns from the patient's information that tie predictor's variables in ahealth science database is the topic of data mining.

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