Diabetes Mellitus Prediction and Diet Recommendation
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10.5120/ijca2021921830 |
Aishwarya Jadhav, Kajal Kadam, Vishal Dusane, Gopal Kabra and Ganesh Deshmukh. Diabetes Mellitus Prediction and Diet Recommendation. International Journal of Computer Applications 183(43):6-11, December 2021. BibTeX
@article{10.5120/ijca2021921830,
author = {Aishwarya Jadhav and Kajal Kadam and Vishal Dusane and Gopal Kabra and Ganesh Deshmukh},
title = {Diabetes Mellitus Prediction and Diet Recommendation},
journal = {International Journal of Computer Applications},
issue_date = {December 2021},
volume = {183},
number = {43},
month = {Dec},
year = {2021},
issn = {0975-8887},
pages = {6-11},
numpages = {6},
url = {http://www.ijcaonline.org/archives/volume183/number43/32217-2021921830},
doi = {10.5120/ijca2021921830},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Type 2 diabetes is a lifelong disease that keeps your body from using insulin the way it should. People with type 2 diabetes are said to have insulin resistance. Insulin resistance, a condition in which fat, muscle, and liver cells do not consume insulin properly leading to this form of diabetes. In this paper, the system is designed to predict diabetes and recommend the diet to the user. Classification algorithms were studied and implemented to calculate accuracy. Based on the results obtained, the highest accuracy was given by the Random Forest classifier. Thus, the same is used in the system for prediction purposes. Diet is recommended to diabetes-affected patients using a diet recommendation module to control his/her blood sugar levels. The recommended diet is based on an individual's requirement of calories calculated by the Harris-Benedict equation. A unified system that predicts whether an individual is diabetic and helps to manage diet according to his/her caloric needs is implemented.
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Keywords
Diabetes mellitus, prediabetes, machine learning, diet recommendation, random forest classifier, disease prediction