Enhancing Liver Disease Diagnosis with Hybrid SMOTEENN Balanced Machine Learning Models: An Empirical Analysis on Indian Patient Liver Disease Datasets

Ritu Rani ¹ Garima Jaiswal ² Nancy . ¹ Lipika . ¹ Shashi Bhushan ³ Fasee Ullah ³ PRABHISHEK SINGH ² Manoj Diwakar ^4*

• ¹ Indira Gandhi Delhi Technical University for Women, New Delhi, NCT of Delhi, India
• ² Bennett University, Greater Noida, Uttar Pradesh, India
• ³ University of Technology Petronas, Tronoh, Perak, Malaysia
• ⁴ Graphic Era University, Dehradun, India

The liver is one of the vital organs of human body that performs some of the most crucial biological processes such as protein and biochemical synthesis, which is required for digestion and cleansing. A large number of patients are suffering from liver disease and hence it has become a life-threatening issue around the world. Annually, around 2 million people die because of liver disease, this accounts for around 4% of all deaths, due to factors like obesity, undiagnosed hepatitis, and excessive alcohol consumption. These factors accumulate and deteriorate the liver condition. Immediate action is necessary for timely diagnosis of the ailment before irreversible damage is done. The work aims to evaluate some of the prominent machine learning algorithms for diagnosing and predicting chronic liver disease. Also, real-world datasets often have imbalanced class distributions, causing classifiers to perform poorly, leading to low accuracy, precision, recall values and high misclassification. The Indian Patient Liver Disease (ILPD) datasets also face an imbalance issue. This work presents two hybrid models, namely SMOTEENN-KNN and SMOTEENN-AdaBoost, which can robustly handle the problem of imbalance in real-world datasets, in addition to improving the accuracy of liver disease prediction. We have also designed a hybrid model which involves the combination of Recursive Feature Elimination (RFE) for feature selection, SMOTE-ENN to tackle the problem of data imbalance and Ensemble learning for enhanced predictions. This hybrid Ensemble model outperforms the other state of art works done and gives an accuracy score of 93.2%, Brier Score loss of 0.032. The research work highlights the potential of data balancing techniques and Ensemble models to improve predictive accuracy in liver disease diagnosis.