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Early diabetes risk classification using supervised learning algorithms | IEEE Conference Publication | IEEE Xplore

Early diabetes risk classification using supervised learning algorithms


Abstract:

Diabetes is one of the most devastating diseases and affects many people. Diabetes can be caused by a variety of causes, including ageing, obesity, inactivity, genetics, ...Show More

Abstract:

Diabetes is one of the most devastating diseases and affects many people. Diabetes can be caused by a variety of causes, including ageing, obesity, inactivity, genetics, a poor diet, high blood pressure, and others. Diabetes increases the likelihood of developing several illnesses, including heart disease, renal disease, stroke, eye problems, nerve damage, etc. The information needed to diagnose diabetes is currently gathered through a variety of tests used in hospitals, and the diagnosis is then used to determine the best course of treatment. The healthcare sector has a considerable application for machine learning (ML). Databases in the healthcare sector are very vast. Big datasets can be examined using ML techniques to find hidden information and patterns, allowing one to learn from the data and predict outcomes properly. Using the existing methods, the forecast’s accuracy is not very good. In this study, we proposed an early diabetes prediction model that incorporates several extrinsic characteristics that contribute to the development of diabetes together with more widely used measures like polyuria, weight loss, polyphagia, visual blurring, alopecia, obesity, etc. The Support Vector Machine (SVM), the Logistic Regression (LOR), the Boosted Tree (BOT), and the Bagged Tree (BAT) are four different classifiers that are utilized in this paper to predict diabetes early on. The device’s performance is assessed in terms of accuracy, recall, specificity, precision, and f-measure. Results show that among the classifiers, BAT has the highest accuracy, at 98%.
Date of Conference: 05-06 May 2023
Date Added to IEEE Xplore: 08 June 2023
ISBN Information:
Conference Location: Gharuan, India

I. Introduction

Diabetes is a long-term condition marked by hyperglycemia, or high blood sugar, which can result in amputation, cardiovascular disease, and other problems like blindness. There will probably be 642 million diabetic people globally in 2040, predicts the International Diabetes Federation (IDF). Therefore, in order to save priceless human lives, there is a pressing need to identify and forecast the symptoms of diabetes early on [1]. Using machine learning techniques to diagnose this condition is one option. Machine learning has quickly spread over many areas of healthcare. Machine learning algorithms can identify whether a patient has diabetes or not, by uncovering hidden patterns using diabetes data. The purpose of this study is to compare the performance and effectiveness of various machine learning algorithms in predicting diabetes.

References

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