Classification model for hypertension with diabetes using gradient boosting and feature engineering

Name: Mongkhon Sinsirimongkhon

Address: 333 Moo1, Thasud, Muang, Chiang Rai 57100

Organization : Mae Fah Luang University

Email : library@mfu.ac.th

keyword: Multiclass Classification

MeSH: Machine learning

Classification :.DDC: e-thesis

; Feature Engineering

MeSH: Computer Engineering

MeSH: Hypertension

MeSH: Diabetes

LCSH: Machine learning

LCSH: Computer Engineering

LCSH: Hypertension

LCSH: Diabetes

Abstract: Hypertension and diabetes present significant global health challenges, impacting individual well-being and economies. Early detection and prevention are pivotal in mitigating their adverse effects. Machine learning is widely applied in various industries and has shown promise in healthcare. While machine learning has shown promise in predicting these conditions separately, limited research has focused on their co-occurrence. This study proposes a novel multiclass-classification approach to predict the coexistence of hypertension and diabetes. The methodology encompasses data collection, preprocessing, model construction, validation, and comparison. Various classifiers were employed, including Decision Tree, Support Vector Machines, Random Forests, Extra Trees, Gradient Boosting, and Long Short-Term Memory. Additionally, CTGAN was utilized to address imbalanced datasets. Results demonstrate the effectiveness of the proposed approach. Gradient Boosting emerged as the most successful among the classifiers, achieving an impressive accuracy of 92.21% and an average AUC-ROC of 96.46%. These findings underscore the potential of machine learning in accurately predicting the concurrent presence of hypertension and diabetes. This study’s significance lies in its contribution to understanding and diabetes. This study’s significance lies in its contribution to understanding and predicting complex health conditions, facilitating early intervention and personalized care strategies. The outcomes suggest a promising avenue for healthcare practitioners to enhance proactive management approaches for individuals with both hypertension and diabetes

Mae Fah Luang University. Learning Resources and Educational Media Centre

Address: CHIANG RAI

Email: library@mfu.ac.th

Name: Punnarumol Temdee

Role: Advisor

Created: 2024

Modified: 2025-02-21

Issued: 2025-02-21

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: e-thesis

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Computer Engineering

Grantor: Mae Fah Luang University

©copyrights Mae Fah Luang University

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	139111.pdf	9.07 MB	5	2025-08-24 12:01:30

ใช้เวลา

0.031264 วินาที