Md Mahfuzur Rahman^1,*, Md Solaiman Chowdhury², Mohammad Shorfuzzaman³, Lutful Karim⁴, Md Shafiullah⁵, Farag Azzedin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2501-2525, 2024, DOI:10.32604/cmes.2024.055065 - 31 October 2024

Abstract This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data. Unlike traditional methods, which often lack transparency in decision-making, our approach focuses on early detection, offering a proactive strategy to mitigate the risks of sepsis. By integrating advanced machine learning algorithms with interpretability techniques, our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions. Moreover, we introduce a preference-based matching algorithm to evaluate disease severity, enabling timely interventions guided… More >

Shaleeza Sohail¹, Atif Alvi^2,*, Aasia Khanum³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3211-3225, 2022, DOI:10.32604/cmc.2022.023560 - 07 December 2021

Abstract Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain. Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions. Recently, some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified. However, adaptability is not specifically considered in this domain. This paper presents a new framework based on hybrid statistical More >