An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Chronic diseases; Class imbalance Classification; NAFLD; Predictive modelling; SMOTE
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved.
Arora N.; Srivastava S.; Tripathi A.; Gupta V.
Indonesian Journal of Electrical Engineering and Computer Science, Vol-37, No. 1, pp. 214-222.
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijeecs.v37.i1.pp214-222 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208103725&doi=10.11591%2fijeecs.v37.i1.pp214-222&partnerID=40&md5=a84dd07cd6cbc135d7799a85e5a88d1e
Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India; Srivastava S., School of Sciences, Christ University, Bengaluru, India; Tripathi A., Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, India; Gupta V., School of Sciences, Christ University, Bengaluru, India