This paper is in the following e-collection/theme issue:

Machine Learning (1971) Clinical Information and Decision Making (1807) Decision Support for Health Professionals (1468) Innovations in Cancer Diagnostic and Decision Support (100)

Published on in Vol 7, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13476, first published .
A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

Authors of this article:

Jiangpeng Wu1, 2 Author Orcid Image ;   Xiangyi Zan3 Author Orcid Image ;   Liping Gao3 Author Orcid Image ;   Jianhong Zhao4 Author Orcid Image ;   Jing Fan2 Author Orcid Image ;   Hengxue Shi2 Author Orcid Image ;   Yixin Wan3 Author Orcid Image ;   E Yu5 Author Orcid Image ;   Shuyan Li1, 2 Author Orcid Image ;   Xiaodong Xie6 Author Orcid Image
Article Authors Cited by (27) Tweetations (3) Metrics

Journals

  1. Sandri V, Gonçalves I, Machado das Neves G, Romani Paraboni M. Diagnostic significance of C-reactive protein and hematological parameters in acute toxoplasmosis. Journal of Parasitic Diseases 2020;44(4):785 View
  2. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  3. Zhao J, Wu J, Wei J, Su X, Chai Y, Li S, Wang Z. Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices. Frontiers in Oncology 2021;10 View
  4. Liu H, Tang K, Peng E, Wang L, Xia D, Chen Z. Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models. Cancer Management and Research 2020;Volume 12:13099 View
  5. Zhang P, Wu J, Zhai H, Li S. ABCModeller: an automatic data mining tool based on a consistent voting method with a user-friendly graphical interface. Briefings in Bioinformatics 2021;22(4) View
  6. Bhattacharjee A, Dey J, Kumari P. A combined iterative sure independence screening and Cox proportional hazard model for extracting and analyzing prognostic biomarkers of adenocarcinoma lung cancer. Healthcare Analytics 2022;2:100108 View
  7. Rehman N, Zia M, Meraj T, Rauf H, Damaševičius R, El-Sherbeeny A, El-Meligy M. A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection. Applied Sciences 2021;11(19):9023 View
  8. Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, Zhang K, Mengesha B, Huang J, Wang D, Luo H. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics 2022;23(1) View
  9. Li S, Li M, Wu J, Li Y, Han J, Cao W, Zhou X. Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM. EPMA Journal 2023;14(2):219 View
  10. Huang X, Xie B, Long J, Chen H, Zhang H, Fan L, Chen S, Chen K, Wei Y. Prediction of risk factors for scrub typhus from 2006 to 2019 based on random forest model in Guangzhou, China. Tropical Medicine & International Health 2023;28(7):551 View
  11. Zhai Y, Lin X, Wei Q, Pu Y, Pang Y. Interpretable prediction of cardiopulmonary complications after non-small cell lung cancer surgery based on machine learning and SHapley additive exPlanations. Heliyon 2023;9(7):e17772 View
  12. Choudhary A, Yu J, Kouznetsova V, Kesari S, Tsigelny I. Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites. Metabolites 2023;13(10):1055 View
  13. Bentick K, Runevic J, Akula S, Kyriacou T, Cool P, Andras P. Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy. Methods 2023;220:55 View
  14. Tared S, Khaouane L, Hanini S, Khaouane A, Roubehie Fissa M. Enhancing lung cancer prediction through crow search, artificial bee colony algorithms, and support vector machine. International Journal of Information Technology 2024;16(5):2863 View
  15. Li S, Li M, Wu J, Li Y, Han J, Song Y, Cao W, Zhou X. Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia. Journal of Translational Medicine 2024;22(1) View
  16. Kotoulas S, Spyratos D, Porpodis K, Domvri K, Boutou A, Kaimakamis E, Mouratidou C, Alevroudis I, Dourliou V, Tsakiri K, Sakkou A, Marneri A, Angeloudi E, Papagiouvanni I, Michailidou A, Malandris K, Mourelatos C, Tsantos A, Pataka A. A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer. Cancers 2025;17(5):882 View
  17. Xu J, Yang K, Quan B, Xie J, Zheng Y. A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning. Frontiers in Microbiology 2025;16 View
  18. Yang Y, Huang L, Gu Y, Wang Z, Liu S, Chen Q, Ning W, Hong G. Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests. Scientific Reports 2025;15(1) View
  19. Bernal-Dolores V, Reyes-Ruiz J, Rodríguez-Relingh K, Martínez-Mier G. The mean corpuscular volume (MCV) is a hematological biomarker associated with COVID-19 mortality risk. Biomarkers in Medicine 2025;19(14):577 View

Books/Policy Documents

  1. Bai F, Aruna S, Ashok Kumar S, Maheswari M, Katyal K, Vipat D, Parasar S. Decision-Making Models. View

Conference Proceedings

  1. Mamun M, Farjana A, Al Mamun M, Ahammed M. 2022 IEEE World AI IoT Congress (AIIoT). Lung cancer prediction model using ensemble learning techniques and a systematic review analysis View
  2. Puneet , Chauhan A. 2020 IEEE International Conference for Innovation in Technology (INOCON). Detection of Lung Cancer using Machine Learning Techniques Based on Routine Blood Indices View
  3. R N, S. V. 2023 2nd International Conference on Computational Systems and Communication (ICCSC). Lung Cancer Malignancy detection Using Voting Ensemble Classifier View
  4. Singh D, Khandelwal A, Bhandari P, Barve S, Chikmurge D. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). Predicting Lung Cancer using XGBoost and other Ensemble Learning Models View
  5. Hossain Raju M, Imam T, Islam J, Rakin A, Nayyem M, Uddin M. 2024 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). An Ontological Framework for Lung Carcinoma Prognostication via Sophisticated Stacking and Synthetic Minority Oversampling Techniques View
  6. Jalaja S, Prerita H. 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC). Comparative Analysis of Lung Cancer Detection Using CNN, KNN, and SVM Algorithms: Evaluating Accuracy and Performance View
  7. Soni V, Kumar V, Semwal V. 2024 IEEE Silchar Subsection Conference (SILCON 2024). A Sequential Optimization Ensemble Learning Technique for Lung Cancer Diagnosis View