AI-Driven Optimization for Liver Disease Prediction Using Data Balancing Techniques
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https://doi.org/10.48047/Keywords:
.Abstract
Liver disease affects millions of people worldwide and is a serious global health problem. Better patient outcomes and efficient disease treatment depend on prompt and accurate diagnosis. It has been demonstrated that machine learning (ML) approaches may accurately predict a wide range of medical disorders, including illnesses of the liver. However, the caliber and volume of training data have a major impact on how well machine learning models perform. Sadly, class imbalance affects a lot of datasets, meaning that some classes—like patients with and without diseases—are not fairly represented.
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Amin, Ruhul, Rubia Yasmin, Sabba Ruhi, Md Habibur Rahman, and Md Shamim Reza. "Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms." Informatics in Medicine Unlocked 36 (2023): 101155.
Md, Abdul Quadir, Sanika Kulkarni, Christy Jackson Joshua, Tejas Vaichole, Senthilkumar Mohan, and Celestine Iwendi. "Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease." Biomedicines 11, no. 2 (2023): 581.
Gupta, Ketan, Nasmin Jiwani, Neda Afreen, and D. Divyarani. "Liver Disease Prediction using Machine learning Classification Techniques." In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), pp. 221-226. IEEE, 2022.
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