Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.
Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare....
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/23/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158121115&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158121115 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/23/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158121115 158121115 158121115 10.1155/2022/3113119 158121115 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach. aug: au: Pal, Madhumita Parija, Smita Mohapatra, Ranjan K. Mishra, Snehasish Rabaan, Ali A. Al Mutair, Abbas Alhumaid, Saad Al-Tawfiq, Jaffar A. Dhama, Kuldeep affil: Electronics and Communication Engineering, CV Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India sug: subj: COVID-19 Symptoms COVID-19 Prognosis Bioinformatics Internet of Things Automation Machine Learning Prediction Models Human Artificial Intelligence Comparative Studies COVID-19 Microbiology Logistic Regression Support Vector Machine Random Forest Decision Trees Algorithms Sensitivity and Specificity Descriptive Statistics Academic Medical Centers Saudi Arabia ab: Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods. Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results. From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion. The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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