Authors :
Adetunji Olusogo Julius; Ayeni Olusola Ayokunle; Fasanya Olawale Ibrahim
Volume/Issue :
Volume 6 - 2021, Issue 9 - September
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3zWotLG
Abstract :
Diabetes is a metabolic disorder that results
from deficiency of the insulin secretion to control high
sugar contents in the body system. At early stage,
diabetes can be managed and controlled. Prolong
diabetes leads to complication disorders such as diabetes
retinopathy, angina, heart attack, stroke, atherosclerosis
and even death. Therefore, assessment of diabetic risk
prediction is necessary at early stage by using machine
learning classification techniques based on observed
sample features. The dataset used for this paper was
obtained from Irvine (UCI) repository of machine
learning databases and was analyzed on WEKA
application platform. The dataset contains 520 samples
with 17 distinct attributes. Machine learning algorithms
used as classifier are K-Nearest Neighbors algorithm
(KNN), Support Vector Machine (SVM),
Keywords :
Diabetes is a metabolic disorder that results from deficiency of the insulin secretion to control high sugar contents in the body system. At early stage, diabetes can be managed and controlled. Prolong diabetes leads to complication disorders such as diabetes retinopathy, angina, heart attack, stroke, atherosclerosis and even death. Therefore, assessment of diabetic risk prediction is necessary at early stage by using machine learning classification techniques based on observed sample features. The dataset used for this paper was obtained from Irvine (UCI) repository of machine learning databases and was analyzed on WEKA application platform. The dataset contains 520 samples with 17 distinct attributes. Machine learning algorithms used as classifier are K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM),
Diabetes is a metabolic disorder that results
from deficiency of the insulin secretion to control high
sugar contents in the body system. At early stage,
diabetes can be managed and controlled. Prolong
diabetes leads to complication disorders such as diabetes
retinopathy, angina, heart attack, stroke, atherosclerosis
and even death. Therefore, assessment of diabetic risk
prediction is necessary at early stage by using machine
learning classification techniques based on observed
sample features. The dataset used for this paper was
obtained from Irvine (UCI) repository of machine
learning databases and was analyzed on WEKA
application platform. The dataset contains 520 samples
with 17 distinct attributes. Machine learning algorithms
used as classifier are K-Nearest Neighbors algorithm
(KNN), Support Vector Machine (SVM),
Keywords :
Diabetes is a metabolic disorder that results from deficiency of the insulin secretion to control high sugar contents in the body system. At early stage, diabetes can be managed and controlled. Prolong diabetes leads to complication disorders such as diabetes retinopathy, angina, heart attack, stroke, atherosclerosis and even death. Therefore, assessment of diabetic risk prediction is necessary at early stage by using machine learning classification techniques based on observed sample features. The dataset used for this paper was obtained from Irvine (UCI) repository of machine learning databases and was analyzed on WEKA application platform. The dataset contains 520 samples with 17 distinct attributes. Machine learning algorithms used as classifier are K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM),