Diabetes Prediction Model Comparison between XgBoost and SVM Algorithms

Author:

Harsh Vardhan 1,Harasis Singh 1,Amit Mithal 1

Affiliation:

1. Jaipur Engineering College and Research Centre, Jaipur, Rajasthan, India

Abstract

Diabetes is a global health epidemic. It increases the danger of cardiovascular disease by fourfold in women and around twice in men. ‘Diabetes’ is an umbrella term for a number of different subtypes of the disease. The most common are Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). Compared to men, women are also at a greater risk of retinopathy and neuropathy from diabetes. Pregnancy may worsen pre-existing conditions and lead to significant blindness. It also aggravates pre-existing kidney diseases. Elderly women with type 2 diabetes mellitus (T2DM) and end-stage renal disease have a significantly higher risk of death than men with similar diseases. Women with diabetes have higher chances of suffering a stroke in comparison to women without it. Women are also more likely to develop depression compared to men. The modeling of support vector machines may additionally be a promising classification technique for identifying women among the population with common diseases like polygenic disorder and pre-diabetes. We use different algorithms for classification, XGBoost based on SVM with GridSearchCV predict results with 83.5% accuracy.

Publisher

Naksh Solutions

Subject

General Medicine

Reference8 articles.

1. https://www.who.int/health-topics/diabetes#tab=tab_1

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3. Pavel Hamet, Johanne Tremblay Centre de recherche, Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada, H2X 0A9 Department of Medicine, Université de Montréal, Montréal, Québec, Canada, H3T 3J77, Canada, Artificial intelligence in medicine,

4. International Journal for Research in Engineering Application & Management (IJREAM) ISSN: 2454-9150 Vol-05, Issue-02, May 2019 Prediction of Diabetes Using Support Vector Machine

5. https://docs.microsoft.com/en-us/azure/machine-learning/studio-modulereference/normalizedata#:~:text=Normalization%20is%20a%20technique%20often,of%20values%20or%20losing%20information.

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