Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Healthcare Operational Decisions
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This paper describes a healthcare operational decision making system based on machine learning classifiers to predict the decisions in comparison to the actual decisions made by the doctor during the healthcare operations. Most of the supervised machine learning classification and optimization techniques is utilized in this type of decision making system. This system can help the doctor make the best decisions. We testify this system on caesarian section which is the most commonly performed obstetric operation in the world to help saves mother and baby. This system helps us to predict when we should use surgery. This study explains utilization of machine learning algorithms in determination of medical operation methods. The results show that k nearest neighbors and Random Forest for this case study generates accuracy of 95.00 % respectively.
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