Non-Alcoholic Fatty Liver Disease Prediction with Feature Optimized XGBoost Model
- Title
- Non-Alcoholic Fatty Liver Disease Prediction with Feature Optimized XGBoost Model
- Creator
- Rout A.; Mishra S.; Sharma V.; Chiadika O.D.-M.; Tonukari T.T.; Iwendi C.
- Description
- Non-alcoholic fatty liver disease (NAFLD) is an expanding health threat, posing significant risks for long-term complications. Early detection and intervention are crucial, but traditional diagnostic methods can be expensive and invasive.This study investigates the utilization of machine learning models for predicting liver diseases from various out-sourced datasets..We employed Decision Trees, Random Forests, and Support Vector Machines (SVMs) to predict NAFLD based on various clinical and demographic features. Model performance was evaluated by calculating accuracy, precision,deviation and accuracy-score.All these models achieved promising accuracy levels, ranging from 80% to 90%, showcasing their potential for NAFLD prediction. Among them, XG-Boost demonstrated the highest performance, with an accuracy of 90% and more.This study demonstrates the effectiveness of machine learning models in predicting NAFLD with high accuracy using readily available data. Further research with larger sized and more varied datasets will vindicate these models for real-world application in clinical settings. 2024 IEEE.
- Source
- 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Fatty Liver Diseases; Kaggle; Machine Learning Model; NAFLD; Prediction Model; Support Vector Machines
- Coverage
- Rout A., Kalinga Institute of Industrial Technology, Odisha, India; Mishra S., Kalinga Institute of Industrial Technology, Odisha, India; Sharma V., Kalinga Institute of Industrial Technology, India; Chiadika O.D.-M., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Tonukari T.T., Electronic and Computer Engineering Brunel University, London, United Kingdom; Iwendi C., University of Bolton, School of Creative Technologies, United Kingdom
- Rights
- Restricted Access
- Relation
- ISBN: 979-835030775-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Rout A.; Mishra S.; Sharma V.; Chiadika O.D.-M.; Tonukari T.T.; Iwendi C., “Non-Alcoholic Fatty Liver Disease Prediction with Feature Optimized XGBoost Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19404.