Optimized Predictive Models in Health Care Using Machine Learning

11Optimized Ensembled Model to Predict Diabetes Using Machine Learning

Kamal¹*, AnujKumar Sharma² and Dinesh Kumar²

¹Om Sterling Global University, Hisar, Haryana, India

²BRCM College of Engineering & Technology, Bahal, Haryana, India

Abstract

Being one of the earth’s fastest illnesses, diabetes can cause various serious side effects, including neurotoxicity, diabetes retina, heart problems, and organ disease, which raise the death and illness rates. Diabetes’ severances and associated risk elements include greatly diminished with early diagnosis. It is a difficult task due to the dearth of labelled numbers and the prevalence of aberrations or incomplete information in medical databases that are accurate and helpful for predicting diabetes. Health records are being digitally stored at an exponentially increasing rate as technology and digitization advance. Machine learning is crucial in identifying trends in these health records, offering fascinating insights to medical professionals to help diagnose various illnesses. An ensemble-based machine learning model based on the dataset for diabetic retinopathy is used in the current work. The dataset for diabetic retinopathy is normalized as a first step. The suggested ensemble model is then trained using this normalized dataset. Finally, the proposed model’s performance is compared to machine learning techniques. According to the comparison analysis, singular machine-learning outperforms ensemble machine-learning methods.

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