Abstract
The rising global threat of cancer has gained significant attention within the scientific community. The integration of Lean Healthcare principles with cutting-edge Machine Learning (ML) techniques has emerged as a promising approach to transform healthcare systems and enhance patient care. One of the most critical areas where this integration is making significant strides is in predicting the risk of developing cancer. This article explores the synergy between Lean Healthcare and ML in the context of cancer risk prediction, highlighting its potential to revolutionize early detection and personalized preventive care. Utilizing five distinct Machine Learning models, the analysis leveraged the Cancer Patients dataset, a publicly accessible resource containing 1000 patient profiles with diverse information related to the risk of cancer development in the human body. The study assessed various ML algorithms in cancer detection and also underscored the potential of Industry 4.0 technologies in improving patient outcomes. Assessment using performance metrics like accuracy, sensitivity, and F-score demonstrated AI’s potential in improving detection accuracy. The results have shown that Multilayer Perceptron (MLP), Random Forest (RF) with Principal Component Analysis (PCA), and Logistic Regression (LR) with PCA exhibit significant efficacy in cancer detection. Consequently, the integration of supervised machine learning methods is poised to offer substantial assistance in the early diagnosis and prognosis of cancer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hanahan, D.: Hallmarks of cancer: new dimensions. Cancer Discov. 12(1), 31–46 (2022)
Shahin, M., Chen, F.F., Hosseinzadeh, A., Koodiani, H.K., Shahin, A., Nafi, O.A.: A smartphone-based application for an early skin disease prognosis: towards a lean healthcare system via computer-based vision. Adv. Eng. Inform. 57, 102036 (2023). https://doi.org/10.1016/j.aei.2023.102036
Shahin, M., Chen, F.F., Hosseinzadeh, A., Bouzary, H., Rashidifar, R.: A deep hybrid learning model for detection of cyber attacks in industrial IoT devices. Int. J. Adv. Manuf. Technol. 123(5), 1973–1983 (2022). https://doi.org/10.1007/s00170-022-10329-6
Shahin, M., Chen, F.F., Bouzary, H., Krishnaiyer, K.: Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. Int. J. Adv. Manuf. Technol. 107, 2927–2936 (2020)
Shaheen, M.Y.: Applications of Artificial Intelligence (AI) in healthcare: a review. Sci. Prepr. (2021)
Kilic, A.: Artificial intelligence and machine learning in cardiovascular health care. Ann. Thorac. Surg. 109(5), 1323–1329 (2020)
Yang, C., Zhang, Y.: Delta machine learning to improve scoring-ranking-screening performances of protein-ligand scoring functions. J. Chem. Inf. Model. 62(11), 2696–2712 (2022)
“Cancer Patients Data. https://www.kaggle.com/datasets/rishidamarla/cancer-patients-data. Accessed 12 Mar 2023
Shahin, M., Chen, F.F., Hosseinzadeh, A., Khodadadi Koodiani, H., Bouzary, H., Shahin, A.: Enhanced safety implementation in 5S + 1 via object detection algorithms. Int. J. Adv. Manuf. Technol. 125(7), 3701–3721 (2023). https://doi.org/10.1007/s00170-023-10970-9
Shahin, M., Chen, F.F., Bouzary, H., Hosseinzadeh, A.: Deploying convolutional neural network to reduce waste in production system. In: 51st SME North American Manufacturing Research Conference (NAMRC 51), vol. 35, pp. 1187–1195, August 2023. https://doi.org/10.1016/j.mfglet.2023.08.127
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shahin, M., Maghanaki, M., Chen, F.F., Hosseinzadeh, A. (2024). Integrating Lean Healthcare and Machine Learning for Cancer Risk Prediction. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Advances in Computing Research (ACR’24). ACR 2024. Lecture Notes in Networks and Systems, vol 956. Springer, Cham. https://doi.org/10.1007/978-3-031-56950-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-56950-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56949-4
Online ISBN: 978-3-031-56950-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)