Diabetic is the primary disease growing the ratios for most of the peoples and it causes kidney failure, amputations, loss of sight, lower limb amputation, stroke, and heart problems. It would be caused by many reasons, such as a lack of daily exercise, lifestyle, poor healthy food habits, overweight, genetic, and so on. Human body convert foodstuff into glucose. Diabetes is a set of disease classified by a raised glucose level in the blood. Diabetes Peoples, the pancreas is not supported to released insulin. The aim of this research work to identify diabetic patients in medical fields using software. Secondary diabetes data samples downloaded from the online and applied for the analysis of the proposed work. In this experimental, Radial-Basis Function Neural Network Algorithm (RBFNN), Logistic Regression (LR) and Optimized RBFNN algorithm are implemented. Optimized RBFNN model gives the best level of accuracy compared to the other models.

Topics
Artificial neural networks, Endocrine system, Organs, Medical fields, Amputation, Carbohydrates, Food, Regression analysis
1.
Aiswarya
Mujumdar
, and
V.
Vaidhei
, “
Diabetes Prediction Using Machine Learning Algorithms
”, (
Science Direct-Procedia Computer Science)
, pp.
292
–
299
.
2.
David
Alberts
, and
Lena
Mamykina
, “
Data-Driven Blood Glucose Pattern Classification, and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes
”, (
JMIR Publications
,
2019
), pp.
31
–
49
.
Google Scholar
 
3.
Deepti
Sisodia
, and
Dilip Singh
Sisodia
, “
Prediction of Diabetes using Classification Algorithm
”, (
International Journal on Computational Intelligence and Data Science
,
2018
), pp.
15787
–
1585
.
Google Scholar
 
4.
J.
Sumitha
, “
BRCA Gene Expression Level Analysis for Identification of Breast Cancer using computer-assisted Algorithm
”, (
International Journal of Pharma and Biosciences
,
2018
), pp.
60
–
64
.
Google Scholar
 
5.
Chala
Beyene
, “
Survey on Prediction and Analysis the Occurrence of Heart Disease Using Data Mining Techniques
”, (
International Journal of Pure and Applied Mathematics
,
2018
), pp.
165
–
174
.
Google Scholar
 
6.
Chieh-Chen
Wu
, and
Wen-Chun
Yeh
,
“Prediction of fatty liver disease using machine learning algorithms
, (
Computer Methods and Programs in Biomedicine
”,
2019
), pp.
23
–
29
.
https://doi.org/10.1016/j.cmpb.2018.12.032
Google Scholar
 
7.
Elham
Nikookar
, and
Ebrahim
Naderi
, “
Hybrid Ensemble Framework for Heart Disease Detection and Prediction
”, (
International Journal of Advanced Computer Science and Applications
,
2018
).
Google Scholar
 
8.
Divya
Jain
, and
Vijendra
Singh
, “
Feature selection and classification systems for chronic disease prediction: A review
”, (
Egyptian Informatics Journal,
2018
), pp.
179
–
189
.
https://doi.org/10.1016/j.eij.2018.03.002
Google Scholar
 
9.
A.
El-Houssainy
,
Radya
, and
Ayman S.
Anwar
, “
Prediction of kidney disease stages using data mining algorithms
”, (
Informatics in medicine unlocked
,
2019
), pp.
100178
.
Google Scholar
 
10.
Muhammad
Yusril
,
Helmi
Setyawan
, and
Rolly Maulana
Awangga
, “
K-Nearest neighbor algorithm on implicit feedback to determine SOP
”, (
Research Gate
,
2019
).
Google Scholar
 
11.
Mucahid Mustafa
Saritas
, and
Ali
Yasar
, “
Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification
”, (
International Journal of Intelligent Systems and Applications in Engineering
,
2019
), pp.
88
–
91
.
https://doi.org/10.18201/ijisae.2019252786
Google Scholar
 
12.
Gyeongcheol
Cho
, and JinyeongYim, “
Review of Machine Learning Algorithms for Diagnosing Mental Illness
”, (
Psychiatry Investing.
2019
), pp.
262
–
269
.
https://doi.org/10.30773/pi.2018.12.21.2
Google Scholar
 
13.
J.
Sumitha
, and
S.
Nathiya
, “
A Comparative Study on Breast Cancer Prediction using Optimized Algorithms
”, (
Proceedings - 2nd International Conference on Smart Electronics and Communication
,
2021
).
Google Scholar
 
14.
J.
Sumitha
, and
T.
Devi
, “
Breast Cancer Diagnosis in Analysis of BRCA Gene Using Machine Learning Algorithms
”, (
Pakistan Journal of Biotechnology
,
2016
).
Google Scholar
 
15.
J.
Sumitha
, “
Analysis of Gene Expression Value Using Bio-Inspired Algorithms
”, (
Pakistan Journal of Biotechnology
,
2019
), pp.
115
–
118
.
Google Scholar
 
16.
J.
Sumitha
, and
T.
Devi
,
D.
Ravi
, “
Comparative Study on Gene Expression for Detecting Diseases Using Optimized Algorithm
”, (
International Journal of Human Genetics
,
2017
), pp.
38
–
42
.
https://doi.org/10.1080/09723757.2017.1305722
Google Scholar
 
17.
J.
Sumitha
, and
T.
Devi
, “
Analysis of Expression Level of Breast Cancer Gene Using Machine Learning Algorithms for Diagnosis of Breast Cancer
”, (
International Journal of Pharma and Bioscience
,
2017
), pp.
79
–
85
.
Google Scholar
 
This content is only available via PDF.
©2023 Authors. Published by AIP Publishing.
2023
Author(s)
You do not currently have access to this content.