Enhancing Lung Cancer Prediction through Machine Learning: A Data-Driven Approach
- Resource Type
- Conference
- Authors
- Trivedi, Dhruv; Munshi, Saloni; Diwan, Anjali; Shah, Sejal
- Source
- 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC) Humanitarian Technology Conference (R10-HTC), 2023 IEEE 11th Region 10. :1150-1155 Oct, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Signal Processing and Analysis
Logistic regression
Visualization
Machine learning algorithms
Lung cancer
Machine learning
Predictive models
Prediction algorithms
Lung Cancer
Data Visualization
Logistic Re-gression
Recommendation System
Machine Learning
prediction System
- Language
- ISSN
- 2572-7621
Lung cancer is a major cause of cancer-related deaths globally. Timely detection is vital for better patient outcomes. Machine learning techniques have demonstrated potential in accurately predicting lung cancer likelihood using clinical characteristics. we investigate the effectiveness of logistic regression in identifying lung cancer and evaluate its performance using a publicly available dataset. We specifically focus on a subset of the dataset and compare our results with previous studies. The results indicate that logistic regression can serve as a valuable approach for identifying lung cancer, underscoring the significance of ongoing model evaluation and optimization. This has the potential to assist healthcare providers in making precise and prompt diagnoses. Further research should concentrate on integrating additional clinical variables and validating the model on extensive and diverse datasets. These results provide valuable insights into the development of efficient and reliable lung cancer detection methods that can aid in early diagnosis and treatment.