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Fusion: Practice and Applications
Volume 1 , Issue 2, PP: 49-65 , 2020 | Cite this article as | XML | Html |PDF

Title

Diabetes prediction system using ml & dl techniques

Authors Names :   Nandini Gupta   1 *     Shubhangi Malik   2     Hardik Chawla   3     Surinder Kaur   4  

1  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  guptanandini12345@gmail.com


2  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  shubhangimalik28@gmail.com


3  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  hardikchawla111@gmail.com


4  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  kaur.surinder@bharatividyapeeth.edu



Doi   :   https://doi.org/10.54216/FPA.010201

Received: January 02, 2020 Accepted: March 19, 2020

Abstract :

Diabetes nowadays is a familiar and long-term disease. If a prediction is made early, better treatment can be provided. The preprocessing data approach is extremely useful in predicting the disease at an early stage. "Many tools are used in determining significant characteristics such as selection, Prediction, and association rule mining for diabetes. The principal component analysis method was used to select significant attributes. Our judgments denote a strong association of diabetes with body mass indicator (BMI) and glucose degree. The study implemented logistic regression, decision trees, and ANN techniques to process Pima Indian diabetes datasets and predict whether people at risk have diabetes. It was analyzed that random forest had the best accuracy of 80.52 %. Out of 500 negative records & 268 positive records, our model correctly analyzed 403 records & 216 records, respectively.

Keywords :

Body Mass Indicator; Artificial Neural Network; Logistic Regression; Random Forest

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Cite this Article as :
Nandini Gupta , Shubhangi Malik , Hardik Chawla , Surinder Kaur, Diabetes prediction system using ml & dl techniques, Fusion: Practice and Applications, Vol. 1 , No. 2 , (2020) : 49-65 (Doi   :  https://doi.org/10.54216/FPA.010201)