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Published in:

Volume 8 Issue 3
March-2021
eISSN: 2349-5162

Unique Identifier

JETIR1908657

Page Number

288-295

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Title

A MODEL FOR PREDICTING TYPE-II DIABETES USING MACHINE LEARNING APPROACH

ISSN

2349-5162

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"A MODEL FOR PREDICTING TYPE-II DIABETES USING MACHINE LEARNING APPROACH ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.288-295, March-2021, Available :http://www.jetir.org/papers/JETIR1908657.pdf

Abstract

Diabetes Mellitus (DM) is a metabolic diseases group where the person will have high blood sugar due to the pancreas unable to produce sufficient insulin or the cell’s which are not responding to the insulin produced. Diabetes is a chronic disease and a major public health challenge worldwide. The main drawback is that there is lack of awareness of the people on eating habits. In our country, diabetes patient counts have increased steadily due to this reason. Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning mechanism. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.

Key Words

Deep learning, Diabetes, Heart rate variability, ECG, CNN, LSTM.

Cite This Article

"A MODEL FOR PREDICTING TYPE-II DIABETES USING MACHINE LEARNING APPROACH ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp288-295, March-2021, Available at : http://www.jetir.org/papers/JETIR1908657.pdf

Publication Details

Published Paper ID: JETIR1908657
Registration ID: 226427
Published In: Volume 8 | Issue 3 | Year March-2021
DOI (Digital Object Identifier):
Page No: 288-295
ISSN Number: 2349-5162

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Cite This Article

"A MODEL FOR PREDICTING TYPE-II DIABETES USING MACHINE LEARNING APPROACH ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp288-295, March-2021, Available at : http://www.jetir.org/papers/JETIR1908657.pdf




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