Generic Disease Prediction using Symptoms with Supervised Machine Learning

Abstract

Data Mining and Machine Learning plays most inspiring area of research that become most popular in health organization. It also plays a vital part to uncover new patterns in medicinal science and services association which thusly accommodating for all the parties associated with this field. This project intend to form a diagnostic model of the common diseases based on the symptoms by using data mining technique such as classification in health domain. In this project, we are going to use algorithms like Random forest, Naive Bayes which can be utilized for health care diagnosis. Performances of the classifiers are compared to each other to find out highest accuracy. This also helps us to find out persons who are affected by the infection. The test based on the outcomes of the diseases.

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

“…The number of publications on this phenomenon decreased from 12 in 2019 to 7 in 2020. CAD presence prediction 23 [25] Heart disease prediction 5 [26] Coronary heart disease prediction 24 [27] Heart disease prediction 6 [28] CHD detection 25 [29] CHD prediction 7 [30] CAD prediction 26 [31] CHD prediction 8 [32] predict coronary heart disease 27 [33] prediction of CHD 9 [16] CHD Prediction based on risk factors 28 [34] classification of coronary artery disease medical data sets [1] Accuracy of ML algorithms for predicting clinical events 29 [35] Prediction of CHD [17] methodology of predicting CHD 30 [36] CAD detection [37] CAD detection 31 [2] CHD Prediction [38] prediction of heart diseases 32 [39] Heart Disease Diagnosis [40] prediction of heart diseases 33 [41] CHD prediction [42] CAD diagnosis 34 [43] CHD prediction [44] Prediction of CHD 35 [45] NN-based prediction of CHD [46] Diagnosing CHD 36 [47] Prediction of CHD [48] prediction of heart disease 37 [49] Prediction of CHD [50] CHD Diagnosis…”