Exploration of black boxes of supervised machine learning models: A demonstration on development of predictive heart risk score

Sajid, Mirza Rizwan and Khan, Arshad Ali and Albar, Haitham M. and Noryanti, Muhammad and Sami, Waqas and Bukhari, Syed Ahmad Chan and Wajahat, Iram (2022) Exploration of black boxes of supervised machine learning models: A demonstration on development of predictive heart risk score. Computational Intelligence and Neuroscience, 2022 (5475313). pp. 1-11. ISSN 1687-5265. (Published)

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Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Complex networks; Decision trees; Logistic regression; Neural networks; Support vector regression
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: Center for Mathematical Science
College of Engineering
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 03 Nov 2022 05:03
Last Modified: 17 Jan 2023 02:36
URI: http://umpir.ump.edu.my/id/eprint/35145
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