DiabDoc : Diabetes Prediction System

Author Reg. ID: TIJER_103878

Published Paper Id: TIJER2305404

Published In: Volume 10 Issue 5, May-2023

Abstract: Diabetes is one of the most surprisingly deadly diseases, serious illnesses, and many people suffer from it intentionally or unknowingly around the world. Diabetes is caused by the condensation of excess sugar in the blood. Age, obesity, lack of exercise, hereditary diabetes, lifestyle, poor diet, and high blood pressure. People with diabetes are at increased risk of heart disease, kidney disease, stroke, eye problems, and nerve damage. Numerous computer-based detection systems have been designed but normal identification process for diabetics requires more time and money. With the rise of machine learning, we have the ability to develop solutions to this serious problem. Medical professionals need a reliable predictive framework for analyzing diabetes. Machine learning can be used to study large datasets, find hidden information and patterns, discover knowledge from the data, and predict outcomes accordingly. In this paper, we studied the effectiveness of machine learning algorithms for two different diabetes datasets. We have developed a system that incorporates machine learning algorithms so that users can interact with the system in an effective way, provide the necessary information and observe predictions in their applications. We feeded the data in to various models and tried to compare the better accuracy among then we found Random Forest model promising and embedded our application with that model.

Keywords: diabetes, prediction, machine learning, healthcare, dataset

Downloads: 000144

Page No: 326-331

Country: Mumbai , Maharashtra , India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2305404

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2305404

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