Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Computational Biology

Background:

• Increasing prevalence of chronic diseases like diabetes, driven by aging populations and lifestyle pressures.
• Generation of substantial medical data during diabetic patient hospitalizations necessitates advanced analytical methods.
• Need for discovering hidden patterns in medical data to aid clinical decision-making and improve patient outcomes.

Purpose of the Study:

• To develop an improved deep convolutional neural network (CNN+) algorithm for accurate diabetes prediction.
• To enhance classification accuracy by combining deep CNN's feature extraction with bagging ensemble methods.
• To provide a reliable tool for assisting healthcare professionals in diagnosing and managing diabetes.

Main Methods:

• Development of a hybrid deep learning model, termed "CNN+", integrating deep convolutional neural networks (CNN) with the bagging ensemble algorithm.
• Utilizing the deep CNN for potent feature extraction from diabetic patient datasets.
• Replacing the traditional output layer of deep CNN with the bagging integrated classification algorithm for improved classification performance.

Main Results:

• The proposed "CNN+" algorithm demonstrated superior performance in classifying diabetic patient data compared to traditional CNN and other classification algorithms.
• The integration of deep CNN and bagging effectively leveraged the strengths of both methods, leading to more reliable disease prediction.
• Experimental validation confirmed the enhanced accuracy and effectiveness of the "CNN+" model in predicting diabetes progression.

Conclusions:

• The "CNN+" algorithm offers a significant advancement in predicting diabetes changes, outperforming existing methods.
• This hybrid approach effectively combines feature extraction and classification, providing a robust tool for clinical decision support.
• The findings highlight the potential of advanced AI techniques in managing chronic diseases and improving healthcare efficiency.