ArticleHeliyon2023
An improved AdaBoost algorithm for identification of lung cancer based on electronic nose.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- eNose technologies in the detection of cancer: a systematic review and meta-analysis.The oncologist · 2026Pooled it
- Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis.BioMed research international · 2025Pooled it
- Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.BMC medical research methodology · 2024Article
- Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.BMC psychology · 2024Article
- Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies.Frontiers in public health · 2024Article
- Chemical tongues as multipurpose bioanalytical tools for the characterization of complex biological samples.Biophysics and physicobiology · 2024Review
- Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators.Frontiers in oncology · 2024Article
- The Application of Machine Learning in Predicting the Permeability of Drugs Across the Blood Brain Barrier.Iranian journal of pharmaceutical research : IJPRArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The research developed an improved intelligent enhancement learning algorithm based on AdaBoost, that can be applied for lung cancer breath detection by the electronic nose (eNose). First, collected the breath signals from volunteers by eNose, including healthy individuals and people who had lung cancer. Additionally, the signals' features were extracted and optimized. Then, multi sub-classifiers were obtained, and their coefficients were derived from the training error. To improve generalization performance, K-fold cross-validation was used when constructing each sub-classifier. The prediction results of a sub-classifier on the test set were then achieved by the voting method. Thus, an improved AdaBoost classifier would be built through heterogeneous integration. The results shows that the average precision of the improved algorithm classifier for distinguishing between people with lung cancer and healthy individuals could reach 98.47%, with 98.33% sensitivity and 97% specificity. And in 100 independent and randomized tests, the coefficient of variation of the classifier's performance hardly exceeded 4%. Compared with other integrated algorithms, the generalization and stability of the improved algorithm classifier are more superior. It is clear that the improved AdaBoost algorithm may help screen out lung cancer more comprehensively. Additionally, it will significantly advance the use of eNose in the early identification of lung cancer.
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