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

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.

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Publication Details

Journal
BMC Bioinformatics
Published
2025-10-21
DOI
https://doi.org/10.1186/s12859-025-06263-5
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Önder Tutsoy, Hüseyin Ali Öztürk, Hilmi Erdem Sumbul
BMC Bioinformatics
Liver Disease Diagnosis and Treatment
article

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

Önder Tutsoy, Hüseyin Ali Öztürk, Hilmi Erdem Sumbul
article en

Abstract

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.

BMC BioinformaticsVol. 26(1)
Sağlık Bilimleri Üniversitesi (TR), Adana Science and Technology University (TR)
Openalex Percentile: Top 60%
Liver Disease Diagnosis and Treatment
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