HEALTH CARE SYSTEM USING MACHINE LEARNING

Abstract

we proposed has checking each and every patient heart disease by using naive Bayes classification in machine learning. so, we can be taken up the results of how much percentage for each and every patients and can be calculated in which stage the disease is currently for every patient as a positive information and negative information. Big data is more difficult to work with it by using the most relational database management systems, desktop statistics and visualization packages. So, we can use the machine learning concept for this system. The proposed shows that the machine learning processing model is starts from the data mining perspective. By using classifiers, we are processing heart percentage and values in which they are showing as a confusion matrix. We have proposed a new classification scheme in which they can be effectively improves all of the classification performance in the situation that Approximate coefficient and represented.By implementing these algorithms is used and the health care data which predicts the patient whether they are having heart disease or not. even the training dataset are available. Stent diagnosis of heart disease. Furthermore, the resulting model has a high specificity rate in which these makes it a handy tool for junior cardiologists to screen out every patients who have a high probability of having the disease and they can be transfer those patients to senior cardiologists for further analysis.