Feature-maximum-dependency-based fusion diagnosis method for COPD
- 24 Downloads
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. COPD has become the fourth most lethal disease in the world, and worldwide deaths continue to become more common as a result of COPD. Therefore, it is important to help doctors diagnose COPD more accurately using big data analytics and effective algorithms. In the past, COPD was mainly studied as follows: applying data to determine the impact of a single feature on the disease, such as the effect of FEV1/FVC (forced expiratory volume in the first second/forced vital capacity), and analyzing a case with simple models, such as logistic regression or a support vector machine. Therefore, there are obviously deficiencies in previous studies. First, the impacts of multi-dimensional features on COPD have not been considered comprehensively. Second, there is no fusion of multiple study methods on the diagnosis and prognosis of COPD. Thus, this paper presents a feature-maximum-dependency-based fusion diagnosis method for COPD. First, the MDF-RS (feature maximum dependency-rough set) algorithm is proposed to extract the optimal combination of multi-dimensional features. Second, the integrated model DSA-SVM (direct search simulated annealing-support vector machine) is presented to classify the disease. Finally, the proposed method is experimentally tested. The results show that the algorithms outperform other classic methods.
KeywordsCOPD Feature-maximum-dependency Multi-dimensional feature Integrated learning
The work is partially supported by the National Natural Science Foundation of China (Nos. 61672329, 61373149, 61472233, 61572300, 81273704), Shandong Province Science and Technology Plan Supported Project (No. 2014GGX101026) and Taishan Scholar Fund of Shandong Province (No. TSHW201502038, 20110819). We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the TITAN X GPU used for this research.
- 5.Cheung N (2017) Machine learning techniques for medical analysis. N Engl J Med 26(4):126–132Google Scholar
- 9.Guo HM, Du J, Huang LF (2017) Application of ARIMA model based on R language in predicting incidence of patients with acute exacerbation of chronic obstructive pulmonary disease. Chinese Journal of Health Statistics 34(2):288–289Google Scholar
- 12.Kaneiwa K (2011) A rough set approach to multiple dataset analysis. 11(3):204–215. Elsevier Science PublishersGoogle Scholar
- 14.Li CQ, Tao YX (2017) Application of support vector machine with simulated annealing algorithm in mbr membrane pollution prediction. In: IEEE International Conference on Software Engineering Research, Management and Applications 34(5):211–217Google Scholar
- 16.Liu Y, Zheng Y, Liang S (2016) Urban water quality pre diction based on multi-task multi-view learning. Proceedings of the 25th International Joint Conference on Artificial Intelligence 3(4):251–265Google Scholar
- 19.Mega JL, Braunwald E, Wiviott SD et al (2012) Rivaroxaban in patients with a recent acute coronary syndrome. N Engl J Med 336(1):19–31Google Scholar
- 24.Steyerberg EW (2011) Clinical prediction models. Acta Anaesthesiol Scand 39(105):134–135Google Scholar
- 27.Zhang YP, Gao WC, Zhang B (2017) SA algorithm based integrated fault detection method for insulated track circuits. Railw J 39(4):68–72Google Scholar