Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors...

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Bibliographic Details
Main Authors: Muhammad Fazal Ijaz, Ganjar Alfian, Muhammad Syafrudin, Jongtae Rhee
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Applied Sciences
Subjects:
type 2 diabetes
hypertension
classification
DBSCAN
SMOTE
Random Forest
Internet of Things
Online Access:http://www.mdpi.com/2076-3417/8/8/1325

Internet

http://www.mdpi.com/2076-3417/8/8/1325

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