A decision support system for cost-effective diagnosis

Objective: Speed, cost, and accuracy are three important goals in disease diagnosis. This paper proposes a machine learning-based expert system algorithm to optimize these goals and assist diagnostic decisions in a sequential decision-making setting. Methods: The algorithm consists of three components that work together to identify the sequence of diagnostic tests that attains the treatment or no test threshold probability for a query case with adequate certainty: lazy-learning classifiers, confident diagnosis, and locally sequential feature selection (LSFS). Speed-based and cost-based objective functions can be used as criteria to select tests. Results: Results of four different datasets are consistent. All LSFS functions significantly reduce tests and costs. Average cost savings for heart disease, thyroid disease, diabetes, and hepatitis datasets are 50%, 57%, 22%, and 34%, respectively. Average test savings are 55%, 73%, 24%, and 39%, respectively. Accuracies are similar to or better than the baseline (the classifier that uses all available tests in the dataset). Conclusion: We have demonstrated a new approach that dynamically estimates and determines the optimal sequence of tests that provides the most information (or disease probability) based on a patient's available information.