Selected Publications
Cardiovascular Health Analysis: Machine Learning and Explainable AI to Predict Heart Attacks
Conference Paper
IEEE Access, 2023
A heart attack, medically termed myocardial infarction, occurs due to a blockage of blood flow to the heart, resulting in tissue damage. To enable early diagnosis, machine learning models can play a pivotal role in analyzing data related to heart attacks. This advancement significantly improves patient outcomes and enhances the overall efficiency of healthcare systems. The objective of this study is to reduce heart attack mortality by enabling early detection and risk prediction through advanced machine learning techniques, thereby improving patient outcomes globally and reducing the burden of cardiac diseases. This paper employs statistical methods to predict the occurrence of heart attacks. Based on the outcomes of hypothesis testing, machine learning models such as Random Forest and XGBoost were trained. For XGBoost, the accuracy rate was 98.68%, the precision rate was 98.45%, the recall (sensitivity) rate was 98.71%, and the F1-score was 98.58%. Furthermore, explainable AI techniques such as SHAP (SHapley Additive exPlanations) were utilized to identify the influencing factors in the machine learning model. In conclusion, certain cardiovascular indicators, such as systolic blood pressure, differ significantly between patients who have experienced heart attacks and those who have not. Understanding heart attack risks through cardiovascular analysis and AI models advances personalized medicine and early intervention strategies.
Energy-Efficient Hyperparameter Tuning for Deep Learning Models
Conference Paper
Researchgate. Access, 2023
Proposes a novel approach to hyperparameter optimization that reduces energy consumption without sacrificing model performance.
Machine Learning for Heart Attack Prediction: A Comparative Study
Conference
ICMLA, 2022
Compares various machine learning algorithms for early prediction of heart attacks using clinical datasets.
Additional Research (In Progress or Published)
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Supply Chain Optimization Using Machine Learning
Published on IEEE Xplore
IEEE Xplore, 2024
Investigates advanced machine learning techniques for logistics analysis and demand forecasting to enhance supply chain efficiency and resilience under uncertainty.
Framingham Heart Study: Risk Factor Analysis with XAI
Published on IEEE Xplore
IEEE Xplore, 2024
Extends the Framingham Heart Study by leveraging machine learning models integrated with explainable AI (SHAP, LIME) to uncover critical cardiovascular risk factors.
Sandfly Species Classification via Deep Learning
Under Review
Targeted Q1 Journal
Proposes a deep learning model using genital and pharyngeal morphological image data for accurate classification of sandfly species—supporting vector-borne disease research.
Deep Learning for ECG Signal Analysis
Under Review
Target Journal Submission
Applies convolutional neural networks (CNNs) to raw ECG signal data for automatic detection of cardiac abnormalities, with potential clinical applications in early diagnosis.