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

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. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like "Deep Belief Network (DBN) and Recurrent Neural Network (RNN)". As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

journal_name

Comput Methods Biomech Biomed Engin

journal_title

Computer methods in biomechanics and biomedical engineering

authors

Ampavathi A,Saradhi TV

doi

10.1080/10255842.2020.1869726

subject

Has Abstract

pub_date

2021-01-11 00:00:00

pages

1-23

eissn

1025-5842

issn

1476-8259

pub_type

杂志文章

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