A Robust Decision Support System for Wireless Healthcare Based on Hybrid Prediction Algorithm
Authors: Neelam Sanjeev Kumar
P Nirmalkumar
Journal: Journal of Medical Systems (2019)
Discipline: CLINICAL MEDICINE
Keywords: EHR; Elderly; Healthcare; Machine learning; PCA
Affiliations: Electronics and Communication Engineering, Anna University, Guindy, Chennai, India. ., Electronics and Communication Engineering, Anna University, Guindy, Chennai, India.

Abstract:  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. 

Retracted: Yes

PubPeer comments:
 This Springer Nature article contains a number of *tortured phrases* - nonsensical expressions used instead of established terms. For example, * **bolster vector machine** instead of the established _u2018support vector machineu2019_ * **counterfeit neural** instead of the established _u2018artificial neural (network)u2019_ * **dimensionality decrease** instead of the established _u2018dimensionality reductionu2019_ * **hereditary calculation** instead of the established _u2018genetic algorithmu2019_ * **hereditary programming** instead of the established _u2018genetic programmingu2019_ * **missing esteem** instead of the established _u2018missing valueu2019_ ![file] ![file] On the topic of the paper. To prepare the dataset, the authors need to assign the target value ( normal  abnormal ) to each row of data. How do they do it? (Tables 1 and 2 are reproduced here for the reference.) ![file] ![file] , 
 Retracted [11 May 2022] . Part of a Special Issue on  [Wearable Computing Techniques for Smart Health]  ------------------------ The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation s findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Author Neelam Sanjeev Kumar disagrees with this retraction. Author P. Nirmalkumar has not responded to correspondence regarding this retraction. ,