New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification.

Abstract

Microarray technology has become a vital tool in cardiovascular research, enabling the simultaneous analysis of thousands of gene expressions. This capability provides a robust foundation for heart disease classification and biomarker discovery. However, the high dimensionality, noise, and sparsity of microarray data present significant challenges for effective analysis. Gene selection, which aims to identify the most relevant subset of genes, is a crucial preprocessing step for improving classification accuracy, reducing computational complexity, and enhancing biological interpretability. Traditional gene selection methods often fall short in capturing complex, nonlinear interactions among genes, limiting their effectiveness in heart disease classification tasks. In this study, we propose a novel framework that leverages deep neural networks (DNNs) for optimizing gene selection and heart disease classification using microarray data. DNNs, known for their ability to model complex, nonlinear patterns, are integrated with feature selection techniques to address the challenges of high-dimensional data. The proposed method, DeepGeneNet (DGN), combines gene selection and DNN-based classification into a unified framework, ensuring robust performance and meaningful insights into the underlying biological mechanisms. Additionally, the framework incorporates hyperparameter optimization and innovative U-Net segmentation techniques to further enhance computational performance and classification accuracy. These optimizations enable DGN to deliver robust and scalable results, outperforming traditional methods in both predictive accuracy and interpretability. Experimental results demonstrate that the proposed approach significantly improves heart disease classification accuracy compared to other methods. By focusing on the interplay between gene selection and deep learning, this work advances the field of cardiovascular genomics, providing a scalable and interpretable framework for future applications.