پیوندهای مفید
آمار
| تعداد نشریات | 32 |
| تعداد شمارهها | 856 |
| تعداد مقالات | 8,306 |
| تعداد مشاهده مقاله | 52,793,525 |
| تعداد دریافت فایل اصل مقاله | 9,222,517 |
Strategies for disease diagnosis by machine learning techniques | ||
| Journal of Mathematical Modeling | ||
| مقاله 2، دوره 11، شماره 3، دی 2023، صفحه 441-450 اصل مقاله (952.36 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2023.23678.2114 | ||
| نویسندگان | ||
| Elham Hafezieh* ؛ Ali Tavakoli؛ Mashaallah Matinfar | ||
| University of Mazandaran, Babolsar, Iran | ||
| چکیده | ||
| Machine learning (ML) techniques have become a point of interest in medical research. To predict the existence of a specified disease, two methods K-Nearest Neighbors (KNN) and logistic regression can be used, which are based on distance and probability, respectively. These methods have their problems, which leads us to use the ideas of both methods to improve the prediction of disease outcomes. For this sake, first, the data is transformed into another space based on logistic regression. Next, the features are weighted according to their importance in this space. Then, we introduce a new distance function to predict disease outcomes based on the neighborhood radius. Lastly, to decrease the CPU time, we present a partitioning criterion for the data. | ||
| کلیدواژهها | ||
| Prediction؛ machine learning؛ classification؛ penalized logistic Ridge regression | ||
| مراجع | ||
|
[1] M. Batista, Estimation of the final size of the coronavirus epidemic by the logistic model, medRxiv, 2020. [2] R. Bender, U. Grouven, Ordinal logistic regression in medical research, J. R. Coll. Physicians Lond. 31 (1997) 546. [3] D.P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, Massachusetts, 1999. [4] E. Dritsas, M.Trigka, Supervised machine learning models for liver disease risk prediction, Com- puters 12 (2023) 19. [5] A. Govindu, S. Palwe, Early detection of Parkinson’s disease using machine learning, Procedia Comput. Sci. 218 (2023) 249–261. [6] G.S. Handelman, H.K. Kok, R.V. Chandra, A.H. Razavi, S. Huang, M. Brooks, H. Asadi, Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods, AJR Am J Roentgenol. 212 (2019) 38-43. [7] A.E. Hoerl, R.W. Kennard, Ridge regression: Biased estimation for nonorthogonal problems, Tech- nometrics 12 (1970) 55–67. [8] T. Huang, D. Le, L. Yuan, S. Xu, X. Peng, Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit, PLOS one 18 (2023) e0280606. [9] M.T. Huyut, Automatic detection of severely and mildly infected COVID-19 patients with supervised machine learning models, IRBM 44 (2023) 100725. [10] C. Iwendi, C.G. Y. Huescas, C. Chakraborty, S. Mohan, COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients, J. Exp. Theor. Artif. Intell., 2022, https://doi.org/10.1080/0952813X.2022.2058097. [11] P.M. Kumar, U.D. Gandhi, A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases, Comput. Electr. Eng. 65 (2018) 222-235. [12] J.P. Li, A.U. Haq, S.U. Din, J. Khan, A. Khan, A. Saboor, Heart disease identification method using machine learning classification in e-healthcare, IEEE Access 8 (2020) 107562–107582. [13] G. D. Magoulas, A. Prentza, Machine Learning in Medical Applications, In Advanced course on artificial intelligence, Springer, Berlin, Heidelberg, 1999. [14] R. Tibshirani, Regression shrinkage and selection via the lasso, J. Roy. Stat. Soc. B (Methodol.) 58 (1996) 267–288. [15] G. Yu, Y. Liu, D. Shen, Graph-guided joint prediction of class label and clinical scores for the Alzheimers disease, Brain Struct. Funct. 221 (2016) 3787–3801. [16] A. Zeb, E. Alzahrani, V.S. Erturk, G. Zaman, Mathematical model for coronavirus disease 2019 (COVID-19) containing isolation class, Biomed Res. Inter. 2020 (2020) 3452402. [17] J. Zhou, A.H. Gandomi, F. Chen, A. Holzinger, Evaluating the quality of machine learning expla- nations: A survey on methods and metrics, Electronics 10 (2021) 593. [18] X. Zhu, H.I. Suk, L. Wang, S.W. Lee, D. Shen, Alzheimers Disease Neuroimaging Initiative. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis, Med. Image Anal. 38 (2017) 205–214. | ||
|
آمار تعداد مشاهده مقاله: 377 تعداد دریافت فایل اصل مقاله: 478 |
||