A diagnosis tool for early detection and classification of heart disease in individuals using transformer mechanisms

Heart disease remains a leading cause of mortality, making accurate and efficient prediction tools essential for the general population. In medical diagnosis, deep learning-based approaches have shown significant potential in identifying complex patterns within clinical data.

This paper proposes a transformer-based model, named the Heart-Doctor for self-detection and classification of heart disease in the general population, addressing the critical challenge of early diagnosis and real-life risk assessment. The proposed model, inspired by the transformer architecture, processes 16 key attributes through a multi-layer transformer encoder, leveraging residual connections and deep feature extraction for improved classification. The proposed method utilizes electronic health records (EHR) from Chungbuk National University Hospital (CBNUH), incorporating symptom-based features along with common clinical attributes such as diagnoses and medical history to enhance predictive performance. The proposed model's effectiveness is evaluated by implementing and comparing it with Convolutional Neural Network (CNN) and Random Forest (RF) classifiers.

Experimental results demonstrate that the proposed model achieves 99% accuracy, compared to RF (98.79%) and CNN (97.64%), with superior precision, recall, and F1-scores, making it a highly effective tool for multi-class heart disease classification. To ensure real-world applicability, the study also includes the development of an Android-based application that integrates the model for real-time risk assessment, thereby enabling healthcare professionals to make timely and data-driven clinical decisions.

Consequently, the performance of the proposed transformer-based diagnosis model outperforms other models for heart disease classification, allowing individuals to detect their heart disease symptoms early and independently in real life.