Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model. Comput Methods Biomech Biomed Engin. 2021 Jan 11;:1-23 Authors: Ampavathi A, Saradhi TV Abstract Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient's symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to "Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson's disease, and Alzheimer's disease", from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Her...
Source: Computer Methods in Biomechanics and Biomedical Engineering - Category: Biomedical Engineering Authors: Tags: Comput Methods Biomech Biomed Engin Source Type: research