Open AccessJournal Article10.22214/ijraset.2023.54415

Diabetes Prediction Using ML

30 Jun 2023

- International Journal For Science Techno...

- Vol. 11, Iss: 6, pp 4209-4212

TL;DR: In this article , a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy is presented, which uses feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found.

Abstract: Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest Neighbour, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Decision Tree. The suggested technique works better than conventional methods, providing a more automated and effective method of diabetes detection. It could transform diabetes diagnosis, enhance patient outcomes, and enable individualised treatment plans.

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Most frequently asked questions

1. What are the main types of diabetes?

Diabetes is a chronic disease caused by the body's inability to produce enough insulin or utilize it effectively. There are three main types of diabetes: Type 1 diabetes (IDDM), Type 2 diabetes (NIDDM), and gestational diabetes. Type 1 diabetes occurs when the body cannot produce enough insulin, while Type 2 diabetes occurs when the body cannot use insulin properly. Gestational diabetes is a temporary condition that affects pregnant women and is characterized by elevated blood sugar levels. Each type of diabetes has different causes, symptoms, and treatment options. Understanding the differences between these types of diabetes is crucial for proper diagnosis and management of the disease.

2. What are the categories of machine learning?

Machine learning can be divided into three categories: reinforcement learning, unsupervised learning, and supervised learning. Reinforcement learning involves training models to make choices based on rewards and consequences. Unsupervised learning builds models on unlabeled data to find patterns and structures. Supervised learning trains models using labeled data. Classification is a subset of supervised learning that applies to the current project. Overall, machine learning techniques have shown to be excellent in understanding and predicting diabetes, which has helped to advance medical diagnosis and treatment.

Citations

Journal Article•10.31127/tuje.1434305

A predictive machine learning framework for diabetes

Danjuma Dan-Adam Maza, +2 more

- 17 Apr 2024

- Turkish journal of engineering

TL;DR: A machine learning framework for the prediction of diabetes, expected to aid medical practitioners in the early detection of the disease, is expected to aid medical practitioners in the early detection of the disease.

...read moreread less

References

•Journal Article•10.1016/J.PROCS.2018.05.122

Prediction of Diabetes using Classification Algorithms

Deepti Sisodia, +1 more

- 01 Jan 2018

- Procedia Computer Science

TL;DR: Three machine learning classification algorithms namely Decision Tree, SVM and Naive Bayes are used in this experiment to detect diabetes at an early stage using Pima Indians Diabetes Database which is sourced from UCI machine learning repository.

...read moreread less

797

Proceedings Article•10.1109/ICCMC.2019.8819841

Diabetes Prediction Using Different Machine Learning Approaches

Priyanka Sonar, +1 more

- 27 Mar 2019

TL;DR: The aim of this analysis is to develop a system which might predict the diabetic risk level of a patient with a better accuracy, based on categorization methods as Decision Tree, ANN, Naive Bayes and SVM algorithms.

...read moreread less

189

•Journal Article•10.32628/CSEIT206463

Diabetes Prediction Using Machine Learning

KM Jyoti Rani

- 30 Jul 2020

TL;DR: The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques.

...read moreread less

•Posted Content

Analysis and Prediction of Diabetes Using Machine Learning

S. Saru, +1 more

- 02 Apr 2019

- Social Science Research Network

TL;DR: In this paper, the WEKA software was employed as a mining tool for diagnosing diabetes using the Pima Indian diabetes database from UCI repository used for analysis and the dataset was studied and analyzed to build an effective model that predicts and diagnoses diabetes disease.

...read moreread less

Proceedings Article•10.1109/IECBES48179.2021.9398759

Diabetes Disease Prediction Using Machine Learning Algorithms

Arwatki Chen Lyngdoh, +2 more

- 01 Mar 2021

TL;DR: In this paper, the prediction of Diabetes Disease by performing an analysis of five supervised machine learning algorithms, i.e. K-Nearest Neighbors, Naive Baye, Decision Tree Classifier, Random Forest and Support Vector Machine, was performed.

...read moreread less

Diabetes Prediction Using ML

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Most frequently asked questions

1. What are the main types of diabetes?

2. What are the categories of machine learning?

Citations

A predictive machine learning framework for diabetes

References

Prediction of Diabetes using Classification Algorithms

Diabetes Prediction Using Different Machine Learning Approaches

Diabetes Prediction Using Machine Learning

Analysis and Prediction of Diabetes Using Machine Learning

Diabetes Disease Prediction Using Machine Learning Algorithms

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