A Novel Approach for Best Parameters Selection and Feature Engineering to Analyze and Detect Diabetes: Machine Learning Insights.
Humans are familiar with "diabetes," a chronic metabolic disease that causes resistance to insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to human health since it can gradually cause significant damage to the heart, blood vessels, eyes, kidneys, and nerves. As...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
5/4/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163554738&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163554738 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 5/4/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163554738 163554738 163554738 10.1155/2023/8583210 163554738 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Approach for Best Parameters Selection and Feature Engineering to Analyze and Detect Diabetes: Machine Learning Insights. aug: au: Ali, Md Shahin Islam, Md Khairul Das, A. Arjan Duranta, D. U. S. Haque, Mst. Farija Rahman, Md Habibur affil: Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh sug: subj: Diabetes Mellitus Diagnosis Artificial Intelligence Early Diagnosis Random Forest Algorithms Human Support Vector Machine Logistic Regression Descriptive Statistics Machine Learning Funding Source ab: Humans are familiar with "diabetes," a chronic metabolic disease that causes resistance to insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to human health since it can gradually cause significant damage to the heart, blood vessels, eyes, kidneys, and nerves. As a result, it is critical to recognize diabetes early on to minimize its negative consequences. Over the years, artificial intelligence (AI) technology and data mining methods are playing a crucial role in detecting diabetic patients. Considering this opportunity, we present a fine-tuned random forest algorithm with the best parameters (RFWBP) that is used with the RF algorithm and feature engineering to detect diabetes patients at an early stage. We have employed several data processing techniques (e.g., normalization, conversion into numerical data) to raw data during the prepossessing phase. After that, we further applied some data mining techniques, adding related characteristics to the primary dataset. Finally, we train the proposed RFWBP and conventional methods like the AdaBoost algorithm, support vector machine, logistic regression, naive Bayes, multilayer perceptron, and a regular random forest with the dataset. Furthermore, we also utilized 5-fold cross-validation to enhance the performance of the RFWBP classifier. The proposed RFWBP achieved an accuracy of 95.83% and 90.68% with and without 5-fold cross-validation, respectively. Moreover, the proposed RFWBP is compared with conventional machine learning methods to evaluate the performance. The experimental results confirm that the proposed RFWBP outperformed conventional machine learning methods. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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