#5524. Prediction of Hepatitis Disease Using K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Multi-Layer Perceptron and Random Forest

August 2026publication date
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Journal’s subject area:
Development;
Public Administration;
Computer Science Applications;
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Abstract:
At present, Hepatitis is one of the serious types of disease which causes death around the world. It is responsiblefor inflammation in the human liver. If we can succeed to detect this deadly disease early, we can save many peoples lives from this disease. In this research paper, we have predicted hepatitis disease by using different data mining techniques. Besides this, we have proposed a decent way by which we can improve the performanceof our prediction models. We have handled missing values present in our dataset by removing the observations having missing values.
Keywords:
Data Mining; Hepatitis Disease; KNN; MLP; Naive Bayes; Random Forest; SVM

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