NAFLD Detection Using Natural Gradient Boosting: A Probabilistic Ensemble Approach for Improved Accuracy and Calibration
- Title
- NAFLD Detection Using Natural Gradient Boosting: A Probabilistic Ensemble Approach for Improved Accuracy and Calibration
- Creator
- Chintala, Sridhar; Janapati, RaviChander; Bhardwaj, Payal; Gude, Srihari
- Description
- A growing global health concern, non-alcoholic fatty liver disease (NAFLD) must be accurately and promptly detected to avoid serious complications. This study suggests a model based on Natural Gradient Boosting (NGBoost) for accurate clinical feature-based NAFLD prediction. In contrast to traditional gradient boosting algorithms, NGBoost uses natural gradients to estimate the entire conditional probability distribution of outcomes, which enhances uncertainty quantification and calibration. Using a publicly accessible Kaggle dataset, the models performance was compared to KNN, SVM, and Decision Tree classifiers. According to experimental results, NGBoost outperformed conventional classifiers in terms of precision, recall, and F1 score, achieving the highest accuracy of 92.8%. Excellent discriminative ability was indicated by the ROC curve, and strong generalization ability with minimal overfitting was confirmed by the trainingvalidation loss analysis. These findings demonstrate how NGBoost may be used to support clinical decisions, allowing for earlier detection and treatment. Subsequent research endeavors will investigate the validation of the model on more extensive real-world datasets and broaden its relevance to additional liver-related conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
- Source
- Lecture Notes in Electrical Engineering;Volume;1528 LNEE;pp.252-260
- Date
- 01-01-2026
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Decision Tree; KNN; Machine learning; NGBoost; Non-alcoholic fatty liver disease (NAFLD)
- Coverage
- Chintala S., School of CS&AI, SR University, Telangana, Warangal, India; Janapati R., School of CS&AI, SR University, Telangana, Warangal, India; Bhardwaj P., Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Off-Campus Deoghar, Jharkhand, Deoghar, 814142, India; Gude S., Department of Electrical and Electronics Engineering, Christ (Deemed to be University), Karnataka, Bengaluru, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 18761100; ISBN: 978-981955830-8;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Chintala, Sridhar; Janapati, RaviChander; Bhardwaj, Payal; Gude, Srihari, “NAFLD Detection Using Natural Gradient Boosting: A Probabilistic Ensemble Approach for Improved Accuracy and Calibration,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25447.