ArticleDigital health
Optimizing lung cancer classification through hyperparameter tuning.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial intelligence for lung cancer classification in cytology specimens: A systematic review and diagnostic test accuracy meta-analysis of benign-malignant diagnosis and ADC/SCC/SCLC subtyping.Journal of pathology informatics · 2026Review
- Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.JMIR medical informatics · 2026Article
- Exploring the diagnostic performance of low-dose CT for colorectal cancer assessment: feasibility of artificial intelligence iterative reconstruction.Quantitative imaging in medicine and surgery · 2026Article
- A novel lexicon dictionary and CNN-LSTM employed hybrid approach for sentiment detection of COVID-19 vaccines.Scientific reports · 2026Article
- Optimized Machine Learning Pipeline for Lung Cancer Classification: Feature Reduction and Hyperparameter Tuning.Diagnostics (Basel, Switzerland) · 2026Article
- Gene-based lung cancer detection system through omix data and optimized convolutional neural network.Journal of computer-aided molecular design · 2026Article
- Ensemble CNN for colon cancer detection using histopathological image.Frontiers in oncology · 2026Article
- Predicting and classifying type 2 diabetes using a transparent ensemble model combining random forest, k-nearest neighbor, and neural networks.Scientific reports · 2025Article
- Enhanced Diagnosis of Lung and Colon Cancer Severity Through Deep Feature Analysis Using DenseNet201 and SVM With Histopathological Images: A Super-Resolution Approach.Cancer reports (Hoboken, N.J.) · 2025Article
- Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence.Cancer reports (Hoboken, N.J.) · 2025Article
- Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.Scientific reports · 2025Article
- Advances in modelling the risk of benign and malignant lung nodules.Frontiers in oncology · 2025Review
- A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.