TY - GEN
T1 - Bleak Medical Prognosis of Covid Patients Using Explainable Machine Learning
AU - Sharma, Richa
AU - Pandey, Himanshu
AU - Agarwal, Ambuj Kumar
AU - Srivastava, Dolley
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Today, artificial intelligence (AI) algorithms are used to generate predictions in many high-stakes fields, such as credit risk analysis and medical diagnosis. Therefore, AI systems are having a greater impact on people, yet many cutting-edge technologies are opaque, negating people's 'right to explanation.' To address this issue, experts in the field have created explainable AI to detail the rationale behind an algorithm's prediction. The focus of this article is on creating an explanation system for a predicted outcome and a machine learning prediction model for poor medical prognosis in Covid patients. Many different types of machine learning algorithms, including both foundational and augmented versions, have been implemented. It was shown that the XGboost model provided superior diagnostic performance. This situation of a prediction has been explained using Shapley Additive Explanations (SHAP). The reason the XGBoost machine learning model gives a certain prediction result may be deduced with the use of three statistics charts.
AB - Today, artificial intelligence (AI) algorithms are used to generate predictions in many high-stakes fields, such as credit risk analysis and medical diagnosis. Therefore, AI systems are having a greater impact on people, yet many cutting-edge technologies are opaque, negating people's 'right to explanation.' To address this issue, experts in the field have created explainable AI to detail the rationale behind an algorithm's prediction. The focus of this article is on creating an explanation system for a predicted outcome and a machine learning prediction model for poor medical prognosis in Covid patients. Many different types of machine learning algorithms, including both foundational and augmented versions, have been implemented. It was shown that the XGboost model provided superior diagnostic performance. This situation of a prediction has been explained using Shapley Additive Explanations (SHAP). The reason the XGBoost machine learning model gives a certain prediction result may be deduced with the use of three statistics charts.
KW - Covid-19
KW - Explainable systems
KW - Machine learning
KW - SHAP value interpretation
KW - XAI
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85184819787&partnerID=8YFLogxK
U2 - 10.1109/ICACTA58201.2023.10393824
DO - 10.1109/ICACTA58201.2023.10393824
M3 - Conference contribution
AN - SCOPUS:85184819787
T3 - Proceedings of 3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023
BT - Proceedings of 3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023
PB - Institute of Electrical and Electronics Engineers
T2 - 3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023
Y2 - 6 October 2023 through 7 October 2023
ER -