Pendyala, V.; Kim, H. Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI. Electronics2024, 13, 1025.
Pendyala, V.; Kim, H. Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI. Electronics 2024, 13, 1025.
Pendyala, V.; Kim, H. Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI. Electronics2024, 13, 1025.
Pendyala, V.; Kim, H. Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI. Electronics 2024, 13, 1025.
Abstract
Machine Learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, the project provides insights into the model workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and at times, unsound ranking of the features to make the predictions. The paper therefore argues that it is important for research in applied machine learning to provide insights into explainability of the models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare. A future direction is to investigate methods to quantify the effectiveness of the machine learning models in terms of the insights from their explainability.
Keywords
Explainable AI; Machine Learning; Mental Health; Model Evaluation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
