Machine Learning Approaches for Binary Classification to Discover Liver Diseases using Clinical Data

TL;DR

This study compares various machine learning algorithms for binary classification of liver diseases using clinical data, demonstrating improved accuracy in distinguishing blood donors from non-donors with conditions like hepatitis, fibrosis, and cirrhosis.

Contribution

It applies data preprocessing techniques and compares multiple classifiers, identifying the most effective method for liver disease classification from clinical datasets.

Findings

Support Vector Machine achieved 98.23% accuracy.

Data preprocessing improved classifier performance.

The study aids medical decision-making with better classification methods.

Abstract

For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification.