A Robust Decision Support System for Wireless Healthcare Based on Hybrid Prediction Algorithm.
Analysis of healthcare data becomes a tedious task as large volume of unlabelled information is generated. In this article, an algorithm is proposed to reduce the complexity involved in analysis of healthcare data. The proposed algorithm predicts the health status of elderly from the data collected...
| Published in: | Journal of Medical Systems Vol. 43; no. 6; pp. 1 - 10 |
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| Main Authors: | , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136503274&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136503274 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2019 vid: 43 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136503274 136503274 136503274 10.1007/s10916-019-1304-7 136503274 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Robust Decision Support System for Wireless Healthcare Based on Hybrid Prediction Algorithm. aug: au: Kumar, Neelam Sanjeev Nirmalkumar, P. affil: Electronics and Communication Engineering, Anna University, Guindy, Chennai, India sug: subj: Prediction Algorithms Evaluation Decision Support Techniques Wireless Communications Electronic Health Records Human Health Status In Old Age Health Facilities Factor Analysis Logistic Regression Computers, Portable Aged Data Mining Male Female Machine Learning ROC Curve Sensitivity and Specificity Aged: 65+ years Male Female ab: Analysis of healthcare data becomes a tedious task as large volume of unlabelled information is generated. In this article, an algorithm is proposed to reduce the complexity involved in analysis of healthcare data. The proposed algorithm predicts the health status of elderly from the data collected at health centres by utilizing PCA (principle component analysis) and SVM (support vector machine) algorithms. The performance of proposed algorithm is assessed by comparing it with well-known methods like quadratic Discriminant, linear Discriminant, logistic regression, KNN weighted and SVM medium Gaussian using F-measure. At that point, the pre-prepared information is subjected to the dimensionality decrease process by playing out the Feature Selection errand. So, chosen component analysis are investigated by the proposed work SVM-based enhanced recursive element determination, and its precision is assessed and contrasted with the other customary classifiers, for example, quadratic Discriminant, Linear Discriminant, Logistic Regression, KNN Weighted and SVM Medium Gaussian. Here, we built up a shrewd versatile information module for the remote procurement and transmission of EHR (Electronic Health Record) chronicles, together with an online watcher for showing the EHR datasets on a PC, advanced cell or tablet. So as to characterize the highlights required by clients, we demonstrated the elderly checking system in home and healing facility settings. Utilizing this data, we built up a portable information exchange module in light of a Raspberry Pi. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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