A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMishra, Sushrutaen_US
dc.contributor.authorThakkar, Hiren Kumaren_US
dc.contributor.authorMallick, Pradeep Kumaren_US
dc.contributor.authorTiwari, Prayagen_US
dc.contributor.authorAlamri, Atifen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.organizationKalinga Institute of Industrial Technologyen_US
dc.contributor.organizationSRM Universityen_US
dc.contributor.organizationKing Saud Universityen_US
dc.date.accessioned2022-05-17T06:50:40Z
dc.date.available2022-05-17T06:50:40Z
dc.date.issued2021-09en_US
dc.descriptionFunding Information: This work was supported by the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, through the Vice Deanship of Scientific Research Chairs: Research Chair of Pervasive and Mobile Computing. Publisher Copyright: © 2021 Elsevier Ltd
dc.description.abstractA sustainable healthcare focuses on enhancing and restoring public health parameters thereby reducing gloomy impacts on social, economic and environmental elements of a sustainable city. Though it has uplifted public health, yet the rise of chronic diseases is a concern in sustainable cities. In this work, a sustainable lung cancer detection model is developed to integrate the Internet of Health Things (IoHT) and computational intelligence, causing the least harm to the environment. IoHT unit retains connectivity continuously generates data from patients. Heuristic Greedy Best First Search (GBFS) algorithm is used to select most relevant attributes of lung cancer data upon which random forest algorithm is applied to classify and differentiates lung cancer affected patients from normal ones based on detected symptoms. It is observed during the experiment that the GBFS-Random forest model shows a promising outcome. While an optimal accuracy of 98.8 % was generated, simultaneously, the least latency of 1.16 s was noted. Specificity and sensitivity recorded with the proposed model on lung cancer data are 97.5 % and 97.8 %, respectively. The mean accuracy, specificity, sensitivity, and f-score value recorded is 96.96 %, 96.26 %, 96.34 %, and 96.32 %, respectively, over various types of cancer datasets implemented. The developed smart and intelligent model is sustainable. It reduces unnecessary manual overheads, safe, preserves resources and human resources, and assists medical professionals in quick and reliable decision making on lung cancer diagnosis.en
dc.description.versionPeer revieweden
dc.format.extent14
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMishra, S, Thakkar, H K, Mallick, P K, Tiwari, P & Alamri, A 2021, ' A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection ', Sustainable Cities and Society, vol. 72, 103079 . https://doi.org/10.1016/j.scs.2021.103079en
dc.identifier.doi10.1016/j.scs.2021.103079en_US
dc.identifier.issn2210-6707
dc.identifier.issn2210-6715
dc.identifier.otherPURE UUID: 5060cfae-4bee-4466-bdd8-440e2078a63den_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/5060cfae-4bee-4466-bdd8-440e2078a63den_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85107758518&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/82806820/A_sustainable_IoHT_based_computationally_intelligent_healthcare_monitoring_system_for_lung_cancer_risk_detection.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/114369
dc.identifier.urnURN:NBN:fi:aalto-202205173229
dc.language.isoenen
dc.publisherElsevier BV
dc.relation.ispartofseriesSustainable Cities and Societyen
dc.relation.ispartofseriesVolume 72en
dc.rightsopenAccessen
dc.subject.keywordClassificationen_US
dc.subject.keywordComputational intelligenceen_US
dc.subject.keywordGreedy Best First Search (GBFS)en_US
dc.subject.keywordHeuristicsen_US
dc.subject.keywordInternet of Health Things (IoHT)en_US
dc.subject.keywordLung canceren_US
dc.subject.keywordRandom foresten_US
dc.subject.keywordSustainable healthcareen_US
dc.titleA sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detectionen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi

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