Liver diseases represent a significant global health challenge, impacting millions of individuals and leading to morbidity and mortality due to their often asymptomatic nature. The early detection and accurate diagnosis of liver disorders are critical for effective treatment and management, making it imperative to leverage advanced technologies such as machine learning. As healthcare systems increasingly rely on data-driven solutions, employing robust predictive models for liver disease can transform clinical practices, improve patient outcomes, and reduce the burden on healthcare providers. This thesis presents an investigation into the application of machine learning techniques for the detection of liver diseases using the Indian Liver Patient Records dataset, which includes clinical data from 579 patients. The study meticulously preprocesses the data by addressing class imbalance through the ADASYN algorithm, encoding categorical variables with LabelEncoder, and calculating feature correlations using the Spearman method. A graph-based approach was adopted to extract insights from patient features, enabling the creation of enriched data representations that were subsequently used to train various machine learning classifiers, including HistGradientBoostingClassifier, RandomForestClassifier, and AdaBoostClassifier. The findings of this research reveal substantial improvements in predictive accuracy, with the HistGradientBoostingClassifier achieving an impressive accuracy of 98.49%. The model outperformed existing methodologies, demonstrating the effectiveness of advanced feature extraction techniques and robust data preprocessing strategies in enhancing the reliability of predictions for liver disease diagnosis. This study not only highlights the expanding role of machine learning in healthcare but also serves as a validation of the potential benefits of data-driven approaches in disease management. Despite the promising results, several limitations are acknowledged in this research. The reliance on a specific dataset may restrict the generalizability of the findings, and the methodologies employed may require validation on diverse datasets to confirm their effectiveness across different populations. Additionally, there is a need for further exploration of deep learning techniques and the integration of multimodal data sources to improve diagnostic accuracy. Future research should aim to address these limitations while continuing to expand the understanding and application of machine learning within the realm of liver disease detection and beyond.
