A Multi-Dataset Classification-Based Deep Learning Framework for Electronic Health Records and Predictive Analysis in Healthcare

• URL: http://arxiv.org/abs/2409.16721v1
• Date: Wed, 25 Sep 2024 08:13:39 GMT
• Title: A Multi-Dataset Classification-Based Deep Learning Framework for Electronic Health Records and Predictive Analysis in Healthcare
• Authors: Syed Mohd Faisal Malik, Md Tabrez Nafis, Mohd Abdul Ahad, Safdar Tanweer,
• Abstract summary: This study proposes a novel deep learning predictive analysis framework for classifying multiple datasets. A hybrid deep learning model combining Residual Networks and Artificial Neural Networks is proposed to detect acute and chronic diseases. Rigorous experimentation and evaluation resulted in high accuracies of 93%, 99%, and 95% for retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions, respectively.
• Score: 0.5999777817331317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In contemporary healthcare, to protect patient data, electronic health records have become invaluable repositories, creating vast opportunities to leverage deep learning techniques for predictive analysis. Retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions have shown promising results through the integration of deep learning techniques for classifying diverse datasets. This study proposes a novel deep learning predictive analysis framework for classifying multiple datasets by pre-processing data from three distinct sources. A hybrid deep learning model combining Residual Networks and Artificial Neural Networks is proposed to detect acute and chronic diseases such as heart diseases, cirrhosis, and retinal conditions, outperforming existing models. Dataset preparation involves aspects such as categorical data transformation, dimensionality reduction, and missing data synthesis. Feature extraction is effectively performed using scaler transformation for categorical datasets and ResNet architecture for image datasets. The resulting features are integrated into a unified classification model. Rigorous experimentation and evaluation resulted in high accuracies of 93%, 99%, and 95% for retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions, respectively. The efficacy of the proposed method is demonstrated through a detailed analysis of F1-score, precision, and recall metrics. This study offers a comprehensive exploration of methodologies and experiments, providing in-depth knowledge of deep learning predictive analysis in electronic health records.

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