Diabetes Prediction Using Medical Variables: Analysis & Data Visualization

Anddrew Richmond Thezo

doi:10.56741/esl.v3i01.472

Authors

Anddrew Richmond Thezo
anddrew.thezo@student.sgu.ac.id
Swiss German University

Keywords:

chronic illness, data visualization, diabetes prediction, medical variables

Abstract

Diabetes is a chronic illness that develops when the body either cannot use the insulin that the pancreas produces properly or does not produce enough of it. One hormone that controls blood sugar is insulin. Approximately 48% of all deaths caused by diabetes occurred before the age of 70 in 2019. Diabetes was the direct cause of 1.5 million deaths in 2019 based on the report from WHO (World Health Organization). This study shows the classification of whether someone has diabetes or not using the 8 datasets (medical variables) of age, gender, body mass index (BMI), hypertension (blood pressure), heart disease, smoking history, HbA1c level, and blood glucose level as the risk factors to predict diabetes in patients based on their medical history and demographic information. Furthermore, the result of this study will be presented with analysis and data visualization.

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Author Biography

Anddrew Richmond Thezo, Swiss German University

is a student in the Business & Communication Department at Swiss German University, located in Tangerang, Indonesia. With a keen interest in the intersection of business and communication, Anddrew is actively pursuing knowledge and skills in these fields. (email: anddrew.thezo@student.sgu.ac.id).

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