The application of classification approaches utilizing multi-variable with machine learning methods holds immense implications, particularly in the realm of healthcare and disease prediction. Accurate classification of medical conditions, such as hepatitis, is critical for early diagnosis and timely intervention. In order to identify people based on important hepatitis-related characteristics, this study applies advanced machine learning with statistical techniques. It also examines a real dataset in order to create a reliable early detection predictive model. Through this model, we aspire to raise awareness and guide affected individuals toward timely treatment. The paper focuses on comprehensive data preprocessing, including outlier removal, handling class imbalance problem, missing values and extract highly correlated features in order to improve model performance. In our research paper, we have applied mean/mode imputation technique to deal with missing values. Furthermore, we have used z score approach to detect and remove outliers from out dataset and handle class imbalance problem by using oversampling technique. To identify features that are highly correlated, we have used the embedded feature selection approach in our paper. Classic machine learning algorithms, notably K-Nearest Neighbors (KNN), Naive Bayes (NB) and Random Forest (RF) have employed to predict either a person is affected by hepatitis disease or not. To assess the efficacy of our model, we have utilized the 10-fold cross validation procedure. At 97.44%, we have the highest classification accuracy from RF, with Precession, Recall, F1 score and ROC values of, respectively, 0.99, 0.96, 0.97 and 1.00.

Keywords: Hepatitis, Missing values, Class imbalance problem, Early-stage prediction, Machine learning, KNN, NB, RF, Classification.

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Source of Funding:

This study did not receive any grant from funding agencies in the public, commercial, or not–for–profit sectors.

Competing Interests Statement:

The authors declare no competing financial, professional, or personal interests.

Consent for publication:

The authors declare that they consented to the publication of this study.

Authors' contributions:

All the authors made an equal contribution in the Conception and design of the work, Data collection, Simulation analysis, Drafting the article, and Critical revision of the article. All the authors have read and approved the final copy of the manuscript.

Availability of data and material:

Authors are willing to share data and material according to the relevant needs.