Academic Journal of Computing & Information Science, 2025, 8(3); doi: 10.25236/AJCIS.2025.080305.
Kai Liu
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China
With the changes in environment and lifestyle, the threats of diseases faced by humanity are increasingly growing. In this context, early disease prediction has become a critical issue in the medical field. However, accurate disease diagnosis based on clinical symptoms remains a highly challenging task for healthcare professionals. To address this challenge, data mining technology has demonstrated significant application value in the field of disease prediction. Currently, the amount of data generated annually in the medical field is growing exponentially, and these vast amounts of medical data provide an important foundation for precision medicine. By utilizing advanced data mining techniques, researchers can extract valuable disease characteristic patterns from large medical datasets and establish reliable predictive models. This study innovatively proposes a Sparse Linear Multi-criteria Optimization Classifier (SLMCOC) model for disease prediction. Comparative experiments with classical models such as Decision Trees and KNN demonstrate that SLMCOC achieves higher prediction accuracy. Moreover, its inherent sparsity enables the identification of critical features for classification, thereby enhancing the interpretability of the prediction results.
Disease prediction; Multi-criteria optimization classifier; Sparsity; Linear
Kai Liu. Sparse Linear Multi-criteria Optimization Classifier for Disease Prediction. Academic Journal of Computing & Information Science(2025), Vol. 8, Issue 3: 36-42. https://doi.org/10.25236/AJCIS.2025.080305.
[1] Shi, Y., Peng, Y., Xu, W., et al. Data mining via multiple criteria linear programming: Applications in credit card portfolio management[J].International Journal of Information Technology & Decision Making, 2002,1(1):131-151.
[2] Shi Y. Multiple Criteria Optimization based Data Mining Methods and Applications: A Systematic Survey[J]. Knowledge and Information Systems, 24(3), 369–391.
[3] Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J. Optimization based data mining: theory and applications[M]. Springer, London:DOI:10.1007/978-0-85729-504-0.
[4] Kou G, Peng Y, Chen Z, et al. Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection[J].Information Sciences,2008,179(4):371-381.
[5] He J. et al. Classifications of Credit Cardholder Behavior by using Fuzzy Linear Programming[J]. International Journal of Information Technology and Decision Making,2004,3(4):633-650.
[6] Shi Y., Tian Y., Chen X., Zhang P. Regularized multiple criteria linear programs for classification[J]. Science in China Series F: Information Sciences, 2009, 52(10):1812-1820.
[7] Peng Y., Kou G., Shi Y. and Chen Z. A Multi-Criteria Convex Quadratic Programming Model for Credit Data Analysis[J].Decision Support Systems,2007,44(4):1016-1030.
[8] Zhang Z., Shi Y. and Gao G. (2009b). A Rough Set-based Multiple Criteria Linear Programming Approach for the Medical Diagnosis and Prognosis. Expert Systems with Applications,2008,36(5), 8932–8937.
[9] Chen R., Zhang Z., et al. Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming[J].Journal of Theoretical Biology,2011,269(1):174-180.