Diabetes Prediction using Machine Learning Techniques Article Swipe

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V. A. Jithendra , Reza Mohit , Margam Madhusudhan , Jagadeesh Basavaiah , S. Kusuma ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.36548/jaicn.2023.2.008

Now a day due to hectic schedules and sedentary lifestyle people do not follow the proper diet. Poor diet may lead to diabetes, and which could result in various health issues such as heart attacks, strokes, renal failure, nerve damage, etc. When diabetes is accurately detected in its early stage , it can be effectively treated. By using Machine Learning methods, the problem can be easily detected and a solution could bearrived. Early diabetes detection and prediction can be greatly improved with machine learning (ML) approaches. When it is detected in an early stage, it can be resolved quickly. The objective of this research is to provide prediction using various supervised machine learning methods. Seven algorithms are compared with each other to figure out which is the best. The algorithms are Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, Gradient Boosting. The evaluation results stated that Logistic Regression is more accurate than other algorithms for the given data set with an accuracy of 82%. After selecting the ML model which is more accurate. A User Interface where users can enter the new data and get results was developed and the results to the user were forwarded through WhatsApp along with some suggestions and precautions.

Related Topics

Machine Learning

Decision Tree

Random Forest

Artificial Intelligence

Computer Science

Support Vector Machine

Boosting (Machine Learning)

Concepts

Machine learning Decision tree Naive Bayes classifier Random forest Artificial intelligence Computer science Logistic regression Support vector machine Gradient boosting Boosting (machine learning) Supervised learning Artificial neural network

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Type: article
Language: en
Landing Page: https://doi.org/10.36548/jaicn.2023.2.008
OA Status: diamond
Cited By: 7
Related Works: 10
OpenAlex ID: https://openalex.org/W4382798960

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OpenAlex ID: https://openalex.org/W4382798960

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DOI: https://doi.org/10.36548/jaicn.2023.2.008

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Title: Diabetes Prediction using Machine Learning Techniques

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-06-01

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Authors: V. A. Jithendra, Reza Mohit, Margam Madhusudhan, Jagadeesh Basavaiah, S. Kusuma

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Landing page: https://doi.org/10.36548/jaicn.2023.2.008

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OA status: diamond

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OA URL: https://doi.org/10.36548/jaicn.2023.2.008

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Concepts: Machine learning, Decision tree, Naive Bayes classifier, Random forest, Artificial intelligence, Computer science, Logistic regression, Support vector machine, Gradient boosting, Boosting (machine learning), Supervised learning, Artificial neural network

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Cited by: 7

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Citations by year (recent): 2025: 1, 2024: 5, 2020: 1

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