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 of precision, but also the issue of life and death. A false decision can disastrously impact patients and their families’ lives. The design of the classifications that work on the type of structural parameter selected is a foundation 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 the Artificial Neural Network act as the structural parameters. Tuning of these parameters is a hectic task. 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 each classifier validated. Our method is checked by original-world data sets and present our findings compared to previous 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 in healthcare as an intelligent learning device.