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All Journal International Journal of Electrical, Energy and Power System Engineering (IJEEPSE)
Jack Billie Chandra
Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Application Of Machine Learning K-Nearest Neighbour Algorithm To Predict Diabetes Jack Billie Chandra; Dewi Nasien
International Journal of Electrical, Energy and Power System Engineering Vol. 6 No. 2 (2023): The International Journal of Electrical, Energy and Power System Engineering (I
Publisher : Electrical Engineering Department, Faculty of Engineering, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ijeepse.6.1.117-121

Abstract

Diabetes is a chronic disease characterized by high blood sugar (glucose) levels or above abnormal values. This can occur when the body is no longer able to absorb glucose properly or when the intake of glucose is higher than needed. Glucose is the main energy source for the cells of the human body. Glucose that accumulates over the long term in the body can lead to complications and more serious and life-threatening diseases. As a result, patients with diabetes must be predicted prior to the onset of disease complications. Machine learning is one of the branches of artificial intelligence that can be used to provide predictive value to datasets of diabetic patients. The tested dataset has 390 observations with data on cholesterol levels, glucose, HDL cholesterol, cholesterol ratio, age, gender, blood pressure, BMI, waist and hip width with its ratio, and the patient's height and weight as variables. Predictions are applied using the K-Nearest Neighbor method, which shows an accuracy of 93.58% with a k value of 3, using 20% of all data as test data.
Co-Authors Dewi Nasien