Develop the hybrid Adadelta Stochastic Gradient Classifier with optimized feature selection algorithm to predict the heart disease at earlier stage. (February 2023)

• Record Type: Journal Article
• Authors: Senthil, R.; Narayanan, B.; Velmurugan, K.
• Is Part Of: Measurement. Volume 25(2023)
• Publication Date: 2023-02
• Subjects: HADSGC-HHBS -- Big data -- Machine learning -- Health care -- Performance -- False positive rate -- Space complexity; Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2022.100602
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: The technique of collecting and analyzing a massive quantity of patient data to obtain meaningful information was available in a medical big data analysis. In many fields, including cloud-based medical systems, there are many barriers to big data analysis. The healthcare industry generates a significant amount of heart disease details for the various patients. Most recent research focuses on business models based on big data analysis to improve predictive performance of heart attack data and reduce risk levels for patients. Data storage, however, has been a major challenge; data must be accessed efficiently in multiple locations in a decentralized context. An objective should be to generate a Hybrid Adadelta Stochastic Gradient Classifier-based Healthcare Hash Big Data Storage (HADSGC-HHBS) method of storing and managing clinical information from many places in a distributed setting with the least amount of space and in the shortest amount of time. Data are categorized using a HADSGC-HHBS technique after vast amounts of information have been collected based on certain characteristics. The stochastic Gradient Classification (SGD) algorithm is to classify patient information using a non-convex possible risk target than the Support Vector Machine(SVM) algorithm. A range of data documents is used to assess the proposed HADSGC-HHBS process. Compared to previous approaches, the proposed HADSGC-HHBS process was productive in terms of classification, false positives, and reduced computing complexity.