Heart disease prediction using machine learning

The wide adaptation of computerbased technology in the health care industry resulted in the accumulation of electronic data. Due to the substantial amounts of data, heart disease is the major cause of deaths worldwide. To give treatment for heart disease, a lot of advanced technologies are used. In medical center it is the most common problem because many medical persons do not have equal knowledge and expertise to treat their patient, so they deduce their own decision and as a result it shows poor outcome and sometimes lead to death. To overcome these problems, prediction of heart disease is being done by using machine learning algorithms and data mining techniques, it has become easy to perform automatic diagnosis in hospitals as they are playing vital role in this regard. However, supervised machine learning (ML) algorithms have showcased significant potential in surpassing standard systems for disease diagnosis and aiding medical experts in the early detection of high-risk diseases. In this literature, the aim is to recognize trends across various two types of supervised ML models in disease detection through the examination of performance metrics. The most prominently discussed supervised ML algorithms were Native Bayes (NB), Decision Trees (DT). Native Bayes (NB)is the most adequate at detecting kidney parameters of disease based on blood report of patient. The Naive Bayes (NV),Decision Tree(DT) performed highly at the prediction of heart diseases (Heart attack or Heart Burn).We have used different parameters to predict heart disease. Those parameters are Age, Gender, Cerebral palsy (CP), Gender, Cerebral palsy (CP), Blood Pressure (bp), Fasting blood sugar test (fbs) etc. In our research paper, we have used built in dataset. This paper investigates which technique gives more accuracy in predicting heart disease based on health parameters. Experiment shows that Naïve Bayes has the highest accuracyof 86%.