A Comparative Analysis of Machine-Learning Algorithms to Build a Predictive Model for Diabetes Disease

• Author(s): Olatunde Olukemi Victoria ; Adesomoju Fisayo Adeyemo
• Paper ID: 1702728
• Page: 231-239
• Published Date: 29-05-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 11 May-2021

Abstract

Machine Learning is concerned with the development of algorithms and techniques that allows the computers to learn and gain intelligence based on the past experience. It is a branch of Artificial Intelligence (AI) closely related to statistics. By learning it means that the system is able to identify and understand the input data, so that it can make decisions and predictions based on the data. In this paper, a web based comparative analysis of various machine learning algorithms (Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression) was design in order to recognize accurate model for detecting diabetes disease.

Keywords

Machine Learning, Diabetes diseases, Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression.

Citations

IRE Journals:
Olatunde Olukemi Victoria , Adesomoju Fisayo Adeyemo "A Comparative Analysis of Machine-Learning Algorithms to Build a Predictive Model for Diabetes Disease" Iconic Research And Engineering Journals Volume 4 Issue 11 2021 Page 231-239

IEEE:
Olatunde Olukemi Victoria , Adesomoju Fisayo Adeyemo "A Comparative Analysis of Machine-Learning Algorithms to Build a Predictive Model for Diabetes Disease" Iconic Research And Engineering Journals, 4(11)