Page 80 - GTM-3-1

Global Translational Medicine Evaluating ML models for CAD prediction

35. Mao L. Matthews Correlation Coefficient; 2019. Available Sensors. 2019;19(15):3400.
from: https://leimao.github.io/blog/matthews-correlation- doi: 10.3390/s19153400
coefficient [Last accessed on 2024 Jan 03].
42. Tharwat A, Gaber T, Ibrahim A, Hassanien AE. Linear
36. Schober P, Boer C, Schwarte LA. Correlation coefficients: discriminant analysis: A detailed tutorial. AI Commun.
Appropriate use and interpretation. Anesth Analg. 2017;30:169-190.
2018;126(5):1763-1768.
doi: 10.3233/AIC-170729
doi: 10.1213/ANE.0000000000002864
43. Xanthopoulos P, Pardalos PM, Trafalis TB. Linear
37. Nahm FS. Receiver operating characteristic curve: Overview discriminant analysis. In: Robust Data Mining. SpringerBriefs
and practical use for clinicians. Korean J Anesthesiol.
2022;75(1):25-36. in Optimization. New York: Springer; 2013.
doi: 10.1007/978-1-4419-9878-1_4
doi: 10.4097/kja.21209
44. Peng C, Cheng Q. Discriminative ridge machine: A classifier
38. Viering T, Loog M. The shape of learning curves: A review.
IEEE Trans Pattern Anal Mach Intell. 2023;45(6):7799-7819. for high-dimensional data or imbalanced data. IEEE Trans
Neural Netw Learn Syst. 2021;32(6):2595-2609.
doi: 10.1109/TPAMI.2022.3220744
doi: 10.1109/TNNLS.2020.3006877
39. Bengfort B, Gray L, Bilbro R, et al. Yellowbrick v1.5. Zenodo;
2022. Available from: https://www.scikit-yb.org/en/latest/ 45. Singh A, Prakash BS, Chandrasekaran K. A Comparison of
api/model_selection/learning_curve.html [Last accessed on Linear Discriminant Analysis and Ridge Classifier on Twitter
2024 Jan 04]. Data. In: 2016 International Conference on Computing,
Communication and Automation (ICCCA). Greater Noida,
40. Kanade VA. Logistic Regression: Equation, Assumptions, India: IEEE; 2016. p. 133-138.
Types, and Best Practices. Austin: Spiceworks. Available from:
https://www.spiceworks.com/tech/artificial-intelligence/ doi: 10.1109/CCAA.2016.7813704
articles/what-is-logistic-regression [Last accessed on 46. Akella A, Akella S. Machine learning algorithms for
2022 Apr 18]. predicting coronary artery disease: Efforts toward an open
source solution. Future Science OA. 2021;7(6):FSO698.
41. Rymarczyk T, Kozłowski E, Kłosowski G, Niderla K. Logistic
regression for machine learning in process tomography. doi: 10.2144/fsoa-2020-0206

Volume 3 Issue 1 (2024) 13 https://doi.org/10.36922/gtm.2669