Classification and Prediction of Liver Disease Diagnosis Using Machine Learning Algorithms

Yadav, Hari Shankar; Singhal, Rohit Kumar

doi:10.1109/inocon57975.2023.10101221

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Early Detection: The primary objective is to develop accurate models that can identify individuals at risk of developing liver diseases at an early stage. [62,65]. By evaluating and comparing the performance of these models, we can identify the most effective approach for liver disease risk prediction.…”

Section: Objectivesmentioning

confidence: 99%

Classification Algorithms for Liver Epidemic Identification

Makkena,

Natarajan

2023

EAI Endorsed Trans Perv Health Tech

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Situated in the upper right region of the abdomen, beneath the diaphragm and above the stomach, lies the liver. It is a crucial organ essential for the proper functioning of the body. The principal tasks are to eliminate generated waste produced by our organs, and digestive food and preserve vitamins and energy materials. It performs many important functions in the body, it regulates the balance of hormones in the body filtering and removing bacteria, viruses, and other harmful substances from the blood. In certain dire circumstances, the outcome can unfortunately result in fatality. There exist numerous classifications of liver diseases, based on their causes or distinguishing characteristics. Some common categories of liver disease include Viral hepatitis, Autoimmune liver disease, Metabolic liver disease, Alcohol-related liver disease, Non-alcoholic fatty liver disease, Genetic liver disease, Drug-induced liver injury, Biliary tract disorders. Machine learning algorithms can help identify patterns and risk factors that may be difficult for humans to detect. With this clinicians can enable early diagnosis of diseases, leading to better treatment outcomes and improved patient care. In this research work, different types of machine learning methods are implemented and compared in terms of performance metrics to identify whether a person effected or not. The algorithms used here for predicting liver patients are Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees Classifier. The experimental results showed that the accuracy of various machine learning models-Random Forest classifier-67.4%, K-nearest neighbor-54.8%, XGBoost-72%, Decision tree-65.1%, Logistic Regression-68.0%, support vector machine-65.1%, Extra Trees Classifier-70.2% after applying Synthetic Minority Over-sampling technique.

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Section: Objectivesmentioning

confidence: 99%