Big data dimensionality reduction-based supervised machine learning algorithms for NASH diagnosis

• Published in: BMC bioinformatics Vol. 26; no. 1; p. 256
• Main Authors: Tutsoy, Onder, Ozturk, Huseyin Ali, Sumbul, Hilmi Erdem
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.10.2025; BioMed Central Ltd
• Subjects: Algorithms; Big Data; Bioinformatics; Biomedical and Life Sciences; Blood; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data mining; Dimensionality Reduction; Humans; Life Sciences; Liver; Liver diseases; Machine Learning; Mathematical optimization; Medical advice systems; Medical examination; Microarrays; Neural networks; Neural Networks, Computer; Non-alcoholic Fatty Liver Disease - blood; Non-alcoholic Fatty Liver Disease - diagnosis; Supervised Machine Learning; Type 2 diabetes; Dimension reduction; Feature selection; NASH disease; Supervised machine learning; Big data; Prediction model
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06263-5

Background Identifying the Non-Alcoholic Steatohepatitis (NASH) that can cause liver failure-based morbidity remains a challenging research problem since there is no confirmed and effective approach for its early and accurate diagnosis yet. A large amount of medical data is collected to diagnose the NASH where the majority of them are redundant. Methods This paper initially focuses on selecting the most informative blood test data among the collected big data with the Pearson correlation statistical approach and modified Particle Swarm Optimization with Artificial Neural Networks (PSO-ANN) machine learning algorithm. Then, a gradient based Batch Least Squares (BLS) and a search-based Artificial Bee Colony (ABC) machine learning algorithms are implemented to optimize the NASH prediction models. Confirmed operational NASH diagnosis supervise the statistical and machine learning algorithms to develop accurate prediction models. Results Two machine learning algorithms were trained and also validated with the varying number of selected input features. The results yielded that the trained BLS machine learning model is able to diagnose benign and malignant cases with 100% and 98% accuracies, respectively. The trained ABC machine learning algorithm diagnoses the benign and malignant cases with 90.5% and 94.3% accuracies, respectively.