• Suseendran S S
• Archana K K
• Roganprasath M
• WilsonImmanuel E

This study explores various machine learning models to classify utilizing the Indian Liver Patient Dataset (ILPD) to study liver disease. Our analysis encompassed data preprocessing techniques such as handling missing values, duplicates, and imbalance using SMOTE. We employed feature selection methods including Pearson correlation, Gain Ratio, and Random Forest feature importance to identify relevant features. Subsequently, we developed and evaluated various models of classification, such as Random Forest, Support Vector Machines (SVM), and Logistic Regression, both with all features and with selected features. Moreover, we conducted hyperparameter tuning for Logistic Regression and Random Forest models to enhance their performance. Additionally, an alternative preprocessing approach using iterative imputer and upsampling was investigated, demonstrating its impact on model performance. Our results accentuate the efficiency of machine learning models in precisely categorizing liver disease, with the ensemble model of Support Vector Machine and Random Forest Classifier achieving a notable accuracy of 91.01% after tuning. This study offers insightful information about the use of machine learning methods for liver disease diagnosis, offering potential avenues for improving healthcare decision-making and patient outcomes.

healthcare,liver disease,prediction,machine learning,data analysis

"Prediction of liver disease using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.f481-f490, March-2024, Available :http://www.jetir.org/papers/JETIR2403559.pdf

"Prediction of liver disease using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppf481-f490, March-2024, Available at : http://www.jetir.org/papers/JETIR2403559.pdf