Diabetes Prediction Using Different Machine Learning Techniques.
Diabetes is considered as one of the deadliest and chronic diseases which cause an increase in blood sugar. Many complications occur if diabetes remains untreated and unidentified. The tedious identifying process results in visiting of a patient to a diagnostic centre and consulting doctor. But the...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 206 - 216 |
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| Autores principales: | , , , , |
| Formato: | computer program equations & formulas research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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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=151005957&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151005957 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151005957 151005957 151005957 151005957 ppf: 206 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Diabetes Prediction Using Different Machine Learning Techniques. aug: au: Upendra, S. M., Gayathri Kumar, D. Vijay Girish, B. Teja, Venkat affil: Associate Professor, Dept. of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, Telangana, 500075 sug: subj: Machine Learning Methods Diabetes Mellitus Diagnosis Diagnostic Errors Prevention and Control Diabetes Mellitus Prognosis Prediction Models Human Algorithms Mathematics ROC Curve Logistic Regression Random Forest Decision Trees ab: Diabetes is considered as one of the deadliest and chronic diseases which cause an increase in blood sugar. Many complications occur if diabetes remains untreated and unidentified. The tedious identifying process results in visiting of a patient to a diagnostic centre and consulting doctor. But the rise in machine learning approaches solves this critical problem. The motive of this study is to design a model which can prognosticate the likelihood of diabetes in patients with maximum accuracy. Therefore these machine learning classification algorithms namely Naive Bayes, Logistic regression, Instance Base Method (K-NN), Ensemble Methods (Random Forest & XG-Boost) and Decision Tree are used in this experiment to detect diabetes at an early stage. Experiments are performed on Pima Indians Diabetes Database (PIDD) which is sourced from UCI machine learning repository. The performances of all these algorithms are evaluated on various measures like Precision, Accuracy, F-Measure, and Recall and Confusion Matrix. Accuracy is measured over correctly and incorrectly classified instances. Results obtained show Logistic Regression outperforms with the highest accuracy of 78.30% comparatively other algorithms. These results are verified using Receiver Operating Characteristic (ROC) curves. pubtype: Academic Journal doctype: computer program equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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