Hybrid Machine Learning Approach for Accurate Lung Cancer Prediction Using Structured Data and Medical Imaging
DOI:
https://doi.org/10.52783/jns.v14.3233Keywords:
Lung-Cancer, Machine Learning, Decision Tree Algorithm, Random Forest, Extreme Gradient Boosting, Convolutional neural Networks and Predictive ModelAbstract
A good prediction of lung cancer addresses predicting the right amount of cancer. Cancer is a primary reason of death compared to all diseases. Health information systems are central to universal healthcare globally. Accurate and required data is crucial in public healthcare decision-making, healthcare sector analysis, planning, resource allocation, and monitoring and program evaluation. Cancer is now the most dangerous kinds of disease among the living organisms in our world. In our world, humans are suffering from various cancers, in our world the worldwide burden of cancer has been estimated to have increased. The leading origin of similar cancer mortality is lung cancer. Only when lung-cancer has progressed does it show symptoms. Initial finding of lung cancer is probable with machine learning and deep learning.
Our earlier system, one of the biggest issues with health care organizations are employing biological process to detect the cancer but the physicians were unable to find the right results which are shortening lives. In medical science the patient is undergoing various diagnostic tests, early diagnosis needs a precise and reliable diagnostic method that enables physicians to differentiate lung, breast and other cancer diseases from dangerous ones. Deep neural network is dominant tool for discovery such type of deceases. In the analysis deliberate the different techniques and it’s arrangement.
Hybrid strategy is employed by combining structured data i.e. patient symptoms, medical history with medical images corresponding Computed Tomography scans and X-rays. Structured data is classified by Decision Tree, Random Forest and Extreme Gradient Boosting, whereas medical images are processed by Convolutional Neural Networks. A fusion strategy is utilized to fuse both modalities for an accurate classification model. Therefore, by the assistance of these models we are going to create a precise classification model of cancer prediction for the patient. Application of data mining classification procedures for successful forecast of future events with implementation of the problems encountered by existing system.
Downloads
Metrics
References
Prasad, P.S.S., Nayak, S.K., Krishna, M.V. “enhanced insider threat detection through machine learning approach with imbalanced data resolution” 2024, Journal of Theoretical and Applied Information Technology, 102(3), pp. 914-926
Neelima, S., Govindaraj, M., Subramani, K., ALkhayyat, A., & Mohan, C. (2024). Factors Influencing Data Utilization and Performance of Health Management Information Systems: A Case Study. Indian Journal of Information Sources and Services, 14(2), 146–152. https://doi.org/10.51983/ijiss-2024.14.2.21
G. Srinivas, A. Lakshmanarao, S. Sushma, M. V. Krishna and S. Neelima, "Fake News Detection Using ML and DL Approaches," 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India, 2023, pp. 1322-1325, doi: 10.1109/ICCPCT58313.2023.10245398.
A. Vino A and V. Vijula, "Lung Cancer Detection using Image Processing," 2024 International Conference on Computing and Data Science (ICCDS), Chennai, India, 2024, pp. 1-6, doi: 10.1109/ICCDS60734.2024.10560372.
M. Mamun, M. I. Mahmud, M. Meherin and A. Abdelgawad, "LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model," 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2023, pp. 205-212, doi: 10.1109/SPIN57001.2023.10116075.
3.B. S, P. R and A. B, "Lung Cancer Detection using Machine Learning," 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 539-543, doi: 10.1109/ICAAIC53929.2022.9793061.
Y. Zhang, B. Dai, M. Dong, H. Chen and M. Zhou, "A Lung Cancer Detection and Recognition Method Combining Convolutional Neural Network and Morphological Features," 2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China, 2022, pp. 145-149, doi: 10.1109/CCET55412.2022.9906329.
S. S. Raoof, M. A. Jabbar and S. A. Fathima, "Lung Cancer Prediction using Machine Learning: A Comprehensive Approach," 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 2020, pp. 108-115, doi: 10.1109/ICIMIA48430.2020.9074947.
G. Paliwal and U. Kurmi, "A Comprehensive Analysis of Identifying Lung Cancer via Different Machine Learning Approach," 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), MORADABAD, India, 2021, pp. 691-696, doi: 10.1109/SMART52563.2021.9675304.
R. K. P. Wiratama, E. S. Cahyadi, D. Meshcherekov and D. Purwitasari, "Random Forest based Risk Factor Analysis for Lung Cancer Prediction," 2024 International Conference on Smart Computing, IoT and Machine Learning (SIML), Surakarta, Indonesia, 2024, pp. 62-67, doi: 10.1109/SIML61815.2024.10578224.
D. Singh, A. Khandelwal, P. Bhandari, S. Barve and D. Chikmurge, "Predicting Lung Cancer using XGBoost and other Ensemble Learning Models," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-6, doi: 10.1109/ICCCNT56998.2023.10308301.
V. M. Rachel and S. Chokkalingam, "Efficiency of Decision Tree Algorithm For Lung Cancer CT-Scan Images Comparing With SVM Algorithm," 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2022, pp. 1561-1565, doi: 10.1109/ICOSEC54921.2022.9951896.
K. Naveen, C. Babaiah, M. B. Jayasree, M. Harisha, K. Shanthi and E. S. Nandini, "Lung Cancer Detection Using Convolutional Neural Networks," 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, 2023, pp. 1-4, doi: 10.1109/ASIANCON58793.2023.10270503.
B. V A, S. Thomas, P. C. Philip, A. Thomas, P. Pillai and N. J. P, "Detection of Early Lung Cancer Cases in Patients with COPD Using eNose Technology: A Promising Non-Invasive Approach," 2023 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Kerala, India, 2023, pp. 1-4, doi: 10.1109/RASSE60029.2023.10363510
M. Mamun, A. Farjana, M. Al Mamun and M. S. Ahammed, "Lung cancer prediction model using ensemble learning techniques and a systematic review analysis," 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2022, pp. 187-193, doi: 10.1109/AIIoT54504.2022.9817326.
B. S, P. R and A. B, "Lung Cancer Detection using Machine Learning," 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 539-543, doi: 10.1109/ICAAIC53929.2022.9793061.
K. R. Sekhar, G. R. L. M. Tayaru, A. K. Chakravarthy, B. Gopiraju, A. Lakshmanarao and T. V. S. Krishna, "An Efficient Lung Cancer Detection Model using Convnets and Residual Neural Networks," 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2024, pp. 1-5, doi: 10.1109/ICAECT60202.2024.10469187
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
