An enhanced machine learning approach for effective prediction of IgA nephropathy patients with severe proteinuria based on clinical data

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis...

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Veröffentlicht in:Computers in biology and medicine 2024-05, Vol.173, p.108341, Article 108341
Hauptverfasser: Ying, Yaozhe, Wang, Luhui, Ma, Shuqing, Zhu, Yun, Ye, Simin, Jiang, Nan, Zhao, Zongyuan, Zheng, Chenfei, Shentu, Yangping, Wang, YunTing, Li, Duo, Zhang, Ji, Chen, Chaosheng, Huang, Liyao, Yang, Deshu, Zhou, Ying
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Sprache:eng
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Zusammenfassung:IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN. •BCOINFO-SVM model for predicting IgA nephropathy (IgAN) and dimensionality reduction of medical data features is proposed.•A variant of weighted mean of vectors (INFO) algorithm is proposed to optimize SVM parameters.•This paper combines the INFO algorithm with Cauchy mutation and Opposition-based learning strategies for the first time.•Compared with the traditional model, this model has better prediction accuracy and results.
ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2024.108341