Diabetes Prediction by Classification Using Machine Learning and Deep Learning Techniques

Registration ID: IJNRD_196908

Published ID: IJNRD2305676

DOI: http://doi.one/10.1729/Journal.34404

Siya Srivastava , Shruti Srivastava , Shubha Mishra

Diabetes, Machine learning, Analytics, artificial intelligence.

Diabetes is a serious metabolic illness that can negatively impact every system in the body. The risk factors for this condition are Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc. Diabetic nephropathy, heart stroke, and other illnesses are all made more likely by undiagnosed diabetes. Millions of people around the world are afflicted by this illness. To live a healthy life, it is crucial to catch diabetes early. Due to the significant increase in diabetes cases, this disease is a cause for concern on a global scale. The standard procedure in hospitals is to obtain the data needed for a diabetic diagnosis by a variety of tests, and based on that diagnosis, the proper therapy is given. This large volume of data collected from the patient has some hidden useful information, which can be used to prognosis, diagnose and treat the disease. The revolutionary advancement in the field of Artificial Intelligence (AI) has opened the door to finding useful insights from the large dataset collected from various sources. All the sectors (healthcare, finance, Industrial sector, etc.) have heavily benefitted from it. Machine learning, which creates algorithms capable of learning patterns and decision-making rules from data, is one area where artificial intelligence has a greater impact. In order to extract knowledge from data, machine learning algorithms have been integrated into data mining pipelines, where they can be used in conjunction with conventional statistical techniques. The healthcare sector greatly benefits from Machine learning. Databases in the healthcare sector are very vast. By analysing large datasets using ML, one can learn from the data and make accurate predictions about the future by uncovering hidden patterns and information. The categorization and prediction accuracy of the current approach is not very good. In this study, we suggested a diabetes prediction model that combines a few extrinsic factors that cause diabetes in addition to more common parameters like glucose, body mass index (BMI), age, insulin, etc. Compared to the old dataset, the new dataset improves classification accuracy. Additionally, a pipeline model for diabetes prediction was imposed with the goal of enhancing classification accuracy.

Published Paper   E-Certificate

Siya Srivastava, Shruti Srivastava, & Shubha Mishra (May-2023). Diabetes Prediction by Classification Using Machine Learning and Deep Learning Techniques. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), g599-g601. http://doi.one/10.1729/Journal.34404

Volume 8 Issue 5, May-2023

Pages : g599-g601

Paper Reg. ID: IJNRD_196908

Published Paper Id: IJNRD2305676

Downloads: 000121980

Research Area: Engineering

Country: Kanpur Nagar, Uttar Pradesh, India, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305676.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305676

Crossref DOI: http://doi.one/10.1729/Journal.34404

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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