Lung Cancer Prediction from Text Datasets Using Machine Learning.
Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/14/2022
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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=157989882&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157989882 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/14/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157989882 157989882 157989882 10.1155/2022/6254177 157989882 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lung Cancer Prediction from Text Datasets Using Machine Learning. aug: au: Anil Kumar, C. Harish, S. Ravi, Prabha SVN, Murthy Kumar, B. P. Pradeep Mohanavel, V. Alyami, Nouf M. Priya, S. Shanmuga Asfaw, Amare Kebede affil: Department of Electronics and Communication Engineering, R. L. Jalappa Institute of Technology Doddaballapur, Bangalore, Karnataka 561203, India sug: subj: Lung Neoplasms Prognosis Machine Learning Utilization Data Management Human India Support Vector Machine Lung Neoplasms Economics Software Sensitivity and Specificity Lung Neoplasms Therapy ab: Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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