Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Comput Methods Biomech Biomed Engin. 2024 Jun;27(8):964-979. doi: 10.1080/10255842.2023.2217978. Epub 2023 May 31.
The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, collected data were from 176 patients to investigate NASH disease, and 19 features were extracted. We then sought to find the best combination of features based on different feature selection algorithms such as Feature Forward Selection (FFS), Minimum Redundancy Maximum Relevance (MRMR), and Mutual Information (MI). Finally, we used nine classifier frameworks with different mathematical mechanisms, including random forest (RF), logistic regression (LR), Linear Discriminant Analysis (LDA), AdaBoost, K nearest neighbors (KNN), multilayer perceptron model (MLP), support vector machine (SVM), and decision tree (DT) to estimate NASH disease. Our investigation revealed that the combination of dominant features, namely body mass index (BMI), glutamic pyruvic transaminase (GPT), total cholesterol (TC), high-density lipoprotein (HDL), Ezetimibe, lipoprotein level Lp(a), Loge(Lp(a)), total triglyceride (TG), Creatinine (Cre), HbA1c, Fibrate, and Sex, selected by the MRMR algorithm and classified by the RF method can provide the most appropriate performance based on less computation effort and maximum performance with accuracy, AUC, precision, and recall indices, which are and respectively. This study investigated the configuration of feature selection and classifier that is most suitable for classifying NASH disease based on clinical data and blood factors. The proposed intelligent algorithm based on MRMR and RF classifier can automatically diagnose NASH disease with appropriate performance and present an initial report without any further invasive methods. It also clarifies the diagnostic process and, as a result, the continuation of their prevention and treatment cycle.
NASH 疾病的早期诊断可以降低患者进展的风险和治疗成本。本研究旨在使用先进的机器学习方法提出智能算法的最佳组合，包括基于临床数据和血液因素的不同特征选择和分类。在这项工作中，收集的数据来自 176 名 NASH 患者，提取了 19 个特征。然后，我们试图根据不同的特征选择算法，如特征前向选择（FFS）、最小冗余最大相关性（MRMR）和互信息（MI），找到最佳的特征组合。最后，我们使用了九种具有不同数学机制的分类器框架，包括随机森林（RF）、逻辑回归（LR）、线性判别分析（LDA）、AdaBoost、K 最近邻（KNN）、多层感知器模型（MLP）、支持向量机（SVM）和决策树（DT）来估计 NASH 疾病。我们的研究表明，组合优势特征，即体重指数（BMI）、谷氨酸丙酮酸转氨酶（GPT）、总胆固醇（TC）、高密度脂蛋白（HDL）、依泽替米贝、脂蛋白水平 Lp(a)、Loge(Lp(a))、总甘油三酯（TG）、肌酐（Cre）、HbA1c、贝特类和性别，由 MRMR 算法选择，由 RF 方法分类，可以提供基于更少计算工作量和最大性能的最合适性能，准确性、AUC、精度和召回率指数分别为和。本研究调查了基于临床数据和血液因素的最适合 NASH 疾病分类的特征选择和分类器配置。基于 MRMR 和 RF 分类器的智能算法可以自动诊断 NASH 疾病，具有适当的性能，并提供无需任何进一步侵入性方法的初步报告。它还阐明了诊断过程，并因此延续了他们的预防和治疗周期。
