Development of a Diabetes Diagnosis System Using Machine Learning Algorithms

This paper describes how to develop diabetes diagnosis through the combined use of the support vector machine, the Decision Tree, Naive Bayes, K-nearest and finally, Random Forest (RF) algorithms. These methods are useful to predict diabetes jointly. The appropriateness of ML-depended techniques to...

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Bibliographic Details
Published inInternational journal of distributed systems and technologies Vol. 13; no. 1; pp. 1 - 22
Main Authors Chang, Victor, Kandadai, Keerthi, Xu, Qianwen Ariel, Guan, Steven
Format Journal Article
LanguageEnglish
Published IGI Global 01.01.2022
Subjects
Algorithms
Analysis
Data mining
Hard disks
Information management
Machine learning
United Kingdom
Online AccessGet full text
ISSN1947-3532
1947-3540
DOI10.4018/IJDST.296246

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Summary:This paper describes how to develop diabetes diagnosis through the combined use of the support vector machine, the Decision Tree, Naive Bayes, K-nearest and finally, Random Forest (RF) algorithms. These methods are useful to predict diabetes jointly. The appropriateness of ML-depended techniques to tackle this issue has been revealed. This diabetes diagnosis system using machine-learning algorithms is used to review papers. This project was based on developing python-based code for machine learning algorithms to perform large scales of diabetes analysis. The hardware requirement of machine learning is RAM that is 128 GB DDR4 2133 MHz and 2 TB Hard Disk and needs 512 GB SSD. One standard library is NumPy that uses to support multi-dimensional arrays objects, various components, and matrices. The Random Forest Prediction representing the pictorial visualization of the model and the accuracy for the data analysis using the Random Forest is 76%.
ISSN:1947-3532
1947-3540
DOI:10.4018/IJDST.296246