Revolutionizing Lung Cancer Detection: A High‐Accuracy Machine Learning Framework for Early Diagnosis.
Lung cancer is a deadly disease. According to a report of 2024, it is the primary reason for 1.82 million deaths. Given the high disease burden, early detection of lung cancer is crucial for improving survival rates and implementing effective strategies. This paper is aimed at conducting a systemati...
| Published in: | BioMed Research International Vol. 2025; pp. 1 - 27 |
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| Main Authors: | , , , , , , |
| Format: | equations & formulas pictorial research systematic review tables/charts Journal Article |
| Published: |
Wiley-Blackwell
12/12/2025
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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=190222783&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190222783 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/12/2025 vid: 2025 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 190222783 190222783 190222783 10.1155/bmri/9961773 190222783 ppf: 1 ppct: 26 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Revolutionizing Lung Cancer Detection: A High‐Accuracy Machine Learning Framework for Early Diagnosis. aug: au: Ali, Tahir Muhammad Mir, Azka Rehman, Attique Ur Humayun, Mamoona Shaheen, Momina Alshammari, Rafeef Taresh Suliman Wesley, Hannah affil: Department of Computer Science,, Gulf University for Sciences and Technology,, Mubarak Al-Abdullah, Kuwait sug: subj: Lung Neoplasms Diagnosis Machine Learning Early Detection of Cancer Prediction Models Human Systematic Review PubMed Funding Source Descriptive Statistics Decision Trees Logistic Regression Random Forest Support Vector Machine ab: Lung cancer is a deadly disease. According to a report of 2024, it is the primary reason for 1.82 million deaths. Given the high disease burden, early detection of lung cancer is crucial for improving survival rates and implementing effective strategies. This paper is aimed at conducting a systematic literature review and developing a highly accurate framework for predicting lung cancer effectively. Tollgate methodology has been used for systematic literature review, and quality assessment criteria were applied to select published articles relevant to the research questions. The paper investigates the effectiveness of machine learning in identifying patterns relevant to lung cancer prediction (Q1), examines the pros and cons of current predictive systems (Q2), compares the use of artificial intelligence in lung cancer prediction with traditional methods (Q3), and identifies key features that distinguish lung cancer from patient symptoms (Q4). Machine learning techniques were employed for the proposed framework. Two publicly available, distinct datasets containing clinical features were obtained. Then, the SelectKBest method was used for feature selection, and SMOTE was used to handle class imbalance. Our proposed framework includes a voting ensemble with random forest, support vector machine, and logistic regression with cross‐validation. The results indicate an accuracy of 99% and 92.5% for the first and second datasets, respectively. This study′s systematic literature review, based on four research questions and a machine learning model, exhibits high accuracy in predicting lung cancer. pubtype: Academic Journal doctype: equations & formulas pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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