Predictive Modelling of Liver Disease Using Biochemical Markers and K-Nearest Neighbors Algorithm
Abstract
The incidence of liver cirrhosis-related deaths is on the rise due to increased alcohol consumption, chronic hepatitis infections, and obesity-related liver conditions. Early detection is critical for improving patient outcomes; however, female patients often experience delayed diagnosis. This study aims to develop a predictive model for liver disease using biochemical markers and to investigate gender disparities in diagnostic accuracy. A dataset of 584 patient records from NorthEast Andhra Pradesh, India, was utilized, comprising ten variables per patient, including age, gender, total bilirubin, direct bilirubin, alkaline phosphatase, SGPT, SGOT, total proteins, albumin, and the albumin/globulin ratio. The data were pre-processed by encoding categorical variables and scaling numerical features. The K-Nearest Neighbors (K-NN) algorithm was employed for classification, and performance was evaluated using cross-validation. The model demonstrated variable accuracy across different folds, with accuracy ranging from 57.76% to 73.28%, precision from 58.14% to 70.56%, recall from 57.76% to 73.28%, and F1-score from 57.95% to 70.45%. These results indicate the potential of biochemical markers in predicting liver disease and highlight significant gender disparities in diagnostic accuracy. The study's contributions include the development of a practical predictive tool and the identification of gender-specific diagnostic challenges. Future research should focus on larger, more diverse datasets and explore additional machine learning algorithms to enhance predictive accuracy and address gender disparities in liver disease diagnosis.
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