Predictive method for the detection of hepatitis with various machine learning technique - Mishra, Shantanu; gatait, Ashish; kumar das, Pomit (International Journal of Psychosocial Rehabilitation, 2024)
History: The classification and estimation of data in medical data mining is not just a matter ofprecision, but also the issue of life and death. A false decision can disastrously impact patientsand their families' lives.The design of the classifications that work on the type of structural parameter selected is afoundation of traditional classification problems. If a floating classificator, the rules, history,consequence etc. act as the structural parameters, the distance metric in the classificator KNearest Neighbor (KNN); and the number of hidden layers, weights, and partialities in theArtificial Neural Network act as the structural parameters. Tuning of these parameters is a hectictask.Methods: For each of our datasets we use different classification algorithms including decisiontrees,knn,svm,extra-tree,adaboost,lightgbm and measured the exactness, score and cross of eachclassifier validated.Our method is checked by original-world data sets and present our findings compared toprevious studies' latest results.Performance: The results of our data set study showed that we performed the Decision trees, KNearest and Support Vector Machines more efficiently than the Neural Network.Conclusions: The approach can be used for different disease forecasts and diagnoses inhealthcare as an intelligent learning device.