A Long-Lasting Hybrid Deep Learning Framework for Early Disease Prediction using Convolutional Neural Networks and Random Forest: Look Over and Model
Keywords:
Convolutional Neural Networks (CNN), Deep learning, Disease prediction, Healthcare analytics, Hybrid model, Random Forest (RF)Abstract
As the volume of global healthcare data expands exponentially, the necessity for intelligent, sustainable predictive systems has become critical. Traditional machine learning methodologies often struggle to process the high-dimensional and heterogeneous nature of modern medical datasets effectively. To bridge this gap, this research proposes a robust Hybrid Deep Learning-Based Disease Prediction Model. Their approach synergizes the automatic feature extraction capabilities of Convolutional Neural Networks (CNN) with the ensemble classification strength of a Random Forest (RF) classifier. By substituting the standard fully connected layers of a CNN with a Random Forest, they aim to enhance generalization and reduce the risk of overfitting. When benchmarked against conventional architectures such as CNN-MLP, CNN-LSTM, KNN, and SVM, the proposed hybrid framework demonstrates superior performance. It not only achieves higher diagnostic accuracy but also contributes to the sustainability of healthcare systems by facilitating early detection and reducing the computational burden on clinical decision-making processes.
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