A Novel Integrated Learning Scheme for Predictive Diagnosis of Critical Care燩atient

Machine learning has proven to be one of the efficient solutions for analyzing complex data to perform identification and classification. With a large number of learning tools and techniques, the health section has significantly benefited from solving the diagnosis problems. This paper has reviewed some of the recent scientific implementations on learning-based schemes to find that existing studies of learning have mainly focused on predictive analysis with less emphasis on preprocessing and more inclination towards adopting sophisticated learning schemes that offer higher accuracy at the cost of the higher computational burden. Therefore, the proposed method addresses the concern mentioned above by a novel computational learning model that emphasizes fine-tuning complex medical data and makes it suitable for learning to balance better classification performance and computational complexity. The implementation is carried out using the MIMIC-III dataset, where the proposed system discretizes, the complete model using physician reports and furnished patient information as the first step. It also prepares the data by choosing a specific tuple and its associated field. The second step introduces a novel relatedness function where preprocessing is carried out using word quantization while adopting auto-encoders in deep learning followed by a novel learning-based diagnosis. The outcome exhibits that the proposed system offers better classification performance in reduced processing time in comparison to existing learning schemes.