Ngozi Hope Chinweuba, Baptista Chinyere Chigbu, Assumpta C Aham, Ifeoma E Onyi, Nneka Chinyere Ezeugo, Blessing C Anakpua, Regina Ijeamasi Enebechi, Ijeoma Awa Kalu, Nneka Justina Eze, Christian Sunday Ugwuanyi
Management of work stress among arts and science school administrators in Nigeria using rational emotive occupational health coaching: A randomized controlled trial evaluation
Medicine
lack interpretability artificial intelligence models ie deep learning machine learning rulesbased obstacle widespread adoption healthcare domain absence understandability transparency frequently leads inadequate accountability ii consequent reduction quality predictive results models hand existence interpretability predictions ai models facilitate understanding trust clinicians complex models data protection regulations worldwide emphasize relevance plausibility verifiability ai models predictions response take role tackling challenge designed interpretabilitybased model algorithms achieve humanlike reasoning abilities statistical analysis datasets calculating relative weights variables features medical images patient symptoms relative weights represented importance variables predictive decisionmaking addition relative weights used find positive negative probabilities disease indicated high fidelity explanations hence primary goal model shed light give insights prediction process models well explain model predictions resulted consequently model contributes demonstrating accuracy furthermore two experiments covid19 datasets demonstrated effectiveness interpretability new model