Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
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RESEARCH ARTICLE   (Open Access)
Contents Vol 8 (6)

Optimized Deep Learning Model for Early Cardiomyopathy Classification Using Microarray Gene Expression Data

T. Sangeetha 1*, K. Manikandan 1,  D. Victor Arokia Doss 2

+ Author Affiliations

Journal of Angiotherapy 8 (6) 1-9 https://doi.org/10.25163/angiotherapy.869635

Submitted: 11 April 2024 Revised: 04 June 2024  Published: 07 June 2024 

Abstract

Background: Cardiomyopathy, a leading cause of chronic heart failure, necessitates early diagnosis to improve patient outcomes. Traditional diagnostic methods, both manual and automated, often result in misclassification and inaccurate detection. This study proposes an optimized hyperparameter-tuned deep neural network model, the Protein Synthesis Defined Deep Belief Network (PSDBN), for accurate Cardiomyopathy classification using gene expression data from microarrays. Methods: The study involved preprocessing gene expression data to address missing values through K-Nearest Neighbour imputation, followed by dimensionality reduction using Singular Value Decomposition. Feature extraction was performed using Kernel Principal Component Analysis, and Particle Swarm Optimization was employed for feature selection. The selected features were fed into the PSDBN, which was fine-tuned for optimal performance. Results: The PSDBN model was evaluated on the GSE138678 dataset from the GEO repository. Performance metrics, including precision, recall, and F1-score, were calculated using cross-validation. The PSDBN outperformed traditional models, achieving a precision of X%, recall of Y%, and F1-score of Z%, significantly reducing overfitting and computational complexity. Conclusion: The proposed PSDBN model, with its robust preprocessing, feature extraction, and optimization techniques, demonstrates superior accuracy in classifying Cardiomyopathy types and predicting disease prognosis, offering a promising tool for early diagnosis and improved patient management.

Keywords: Cardiomyopathy, Gene Expression, Deep Belief Network, Particle Swarm Optimization, Machine Learning

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