Advancing cardiovascular disease detection through machine learning: an in-depth analysis and improvement of current methods using a novel hybrid multi-model fuzzy ensemble approach

ความก้าวหน้าในการตรวจหาโรคหัวใจและหลอดเลือดผ่านการเรียนรู้ของเครื่อง: การวิเคราะห์เชิงลึกและการปรับปรุงวิธีการปัจจุบันโดยใช้แนวทาง Fuzzy Ensemble แบบผสมผสานหลายโมเดลแบบใหม่

Name: Manop Chugh

LCSH: Heart -- Examination

LCSH: Heart -- Diseases

LCSH: Machine learning

Abstract: Timely and accurate detection of cardiovascular diseases (CVDs) is crucial for reducing mortality rates. Despite advancements in AI and ML models for CVD detection, low model performance remains a challenge, hindering early CVD detection. This thesis proposes a novel hybrid ensemble learning framework that combines multiple ML algorithms with a fuzzy expert system to enhance CVD diagnosis and prediction accuracy. The framework accommodates a wide range of ML algorithms, offering flexibility in selecting the most suitable ones for a given dataset. Evaluation on standard datasets, UCI Cleveland and Framingham, against ten popular algorithms demonstrates the proposed framework's superiority, achieving higher accuracy, precision, recall, and FI score. Specifically, on the UCI Cleveland dataset, our approach yielded a 3.3% accuracy increase, 0.4% precision increase, 2% recall increase, and 2% FI score increase compared to other algorithms. On the Framingham dataset, our approach achieved a 7% accuracy increase, 7.1% precision increase, 6.1% recall increase, and 6.9% FI score increase. This thesis contributes a promising solution for enhancing CVD diagnosis and prediction accuracy. Future work involves optimizing the framework using diverse ML algorithms and collecting new datasets to study the impact of PM 2.5 air quality on heart disease

King Mongkut's Institute of Technology Ladkrabang. Central Library

Address: BANGKOK

Email: Lifelong@kmitl.ac.th

Name: Isara Anantavrasilp

Role: Thesis Advisor

Email : isara.an@kmitl.ac.th

Created: 2023

Modified: 2024-09-05

Issued: 2024-09-05

วิทยานิพนธ์/Thesis

application/pdf

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Robotics and Computational Intelligence Systems

Grantor: King Mongkut's Institute of Technology Ladkrabang

©copyrights King Mongkut's Institute of Technology Ladkrabang

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