Research on the Application of Integrating Medical Data Intelligence and Machine Learning Algorithms in Cancer Diagnosis
DOI:
https://doi.org/10.71222/gs918h25Keywords:
medical data intelligence, cancer diagnosis, machine learning, multimodal fusion, model optimization, clinical decision support, deep learningAbstract
The exponential growth of medical data has made it increasingly important to unlock its latent clinical value. This paper investigates the integration of medical data intelligence and machine learning algorithms in the context of cancer diagnosis. Key challenges and strategies related to multi-source data fusion, feature extraction, model optimization, and system stability are examined. The study reviews the use of imaging, pathology, genomic, and textual data across various machine learning frameworks and proposes a streamlined, clinically applicable approach to intelligent diagnostic support. The aim is to provide an efficient auxiliary diagnostic tool to advance precision medicine and support clinical decision-making in oncology.
References
1. M. J. Iqbal, Z. Javed, H. Sadia, I. A. Qureshi, A. Irshad, R. Ahmed, and J. Sharifi-Rad, "Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future," Cancer cell international, vol. 21, no. 1, p. 270, 2021. doi: 10.1186/s12935-021-01981-1
2. E. V. Varlamova, M. A. Butakova, V. V. Semyonova, S. A. Soldatov, A. V. Poltavskiy, O. I. Kit, and A. V. Soldatov, "Machine learning meets cancer," Cancers, vol. 16, no. 6, p. 1100, 2024. doi: 10.3390/cancers16061100
3. P. N. Kamalapathy, M. R. Gonzalez, T. M. de Groot, D. Ramkumar, K. A. Raskin, S. Ashkani‐Esfahani, and S. A. Lozano‐Calderón, "Prediction of 5year survival in soft tissue leiomyosarcoma using a machine learning model algorithm," Journal of Surgical Oncology, vol. 129, no. 3, pp. 531-536, 2024. doi: 10.1002/jso.27514
4. S. Bendale, A. Parai, S. Deshpande, A. Iyer, and A. Kumbhare, "Predictive Brain Cancer Detection and Treatment Using Machine Learning and Artificial Intelligence," International Journal of Chemical Separation Technology, vol. 9, no. 1, pp. 28-39, 2023.
5. M. Farsi, "Filter-Based Feature Selection and Machine-Learning Classification of Cancer Data," Intelligent Automation & Soft Computing, vol. 28, no. 1, 2021. doi: 10.32604/iasc.2021.015460
