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Modification of Case-Based Reasoning Similarity Formula to Enhance the Performance of Smart System in Handling the Complaints of in vitro Fertilization Program Patients

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Objectives: Eighty percent of in vitro fertilization (IVF) patients have high anxiety levels, which influence the success of IVFand drive IVF patients to quickly report any abnormal symptoms. Rapid responses from fertility subspecialist doctors mayreduce patients’ anxiety levels, but fertility subspecialist doctors’ high workload and their patients’ worsening health conditionsmake them unable to handle IVF patients’ complaints quickly. Research suggests that smart systems using case-basedreasoning (CBR) can help doctors handle patients quickly. However, a prior study reported enhanced accuracy by modifyingthe CBR similarity formula based on Lin’s similarity theory to generate the Chris case-based reasoning (CCBR) similarityformula. Methods: The data were validated through interviews with two fertility subspecialist doctors, interviews with twoIVF patients, a questionnaire administered to 17 community members, the relevant literature, and 256 records with data onIVF patients’ complaints and how they were handled. An experiment compared the performance of the CBR similarity formulaalgorithm with the CCBR similarity formula algorithm. Results: A confusion matrix showed that the CCBR similarityformula had an accuracy value of 52.58% and a precision value of 100%. Fertility subspecialist doctors stated that 89.69% ofthe CCBR similarity formula recommendations were accurate. Conclusions: We recommend applying a combination of theCCBR similarity formula and a minimum reference value of 80% with a CBR smart system for handling IVF patients’ complaints. This recommendation for an accurate system produced by the CBR similarity formula may help fertility subspecialistdoctors handle IVF patients’ complaints.

Abstract

Objectives: Eighty percent of in vitro fertilization (IVF) patients have high anxiety levels, which influence the success of IVFand drive IVF patients to quickly report any abnormal symptoms. Rapid responses from fertility subspecialist doctors mayreduce patients’ anxiety levels, but fertility subspecialist doctors’ high workload and their patients’ worsening health conditionsmake them unable to handle IVF patients’ complaints quickly. Research suggests that smart systems using case-basedreasoning (CBR) can help doctors handle patients quickly. However, a prior study reported enhanced accuracy by modifyingthe CBR similarity formula based on Lin’s similarity theory to generate the Chris case-based reasoning (CCBR) similarityformula. Methods: The data were validated through interviews with two fertility subspecialist doctors, interviews with twoIVF patients, a questionnaire administered to 17 community members, the relevant literature, and 256 records with data onIVF patients’ complaints and how they were handled. An experiment compared the performance of the CBR similarity formulaalgorithm with the CCBR similarity formula algorithm. Results: A confusion matrix showed that the CCBR similarityformula had an accuracy value of 52.58% and a precision value of 100%. Fertility subspecialist doctors stated that 89.69% ofthe CCBR similarity formula recommendations were accurate. Conclusions: We recommend applying a combination of theCCBR similarity formula and a minimum reference value of 80% with a CBR smart system for handling IVF patients’ complaints. This recommendation for an accurate system produced by the CBR similarity formula may help fertility subspecialistdoctors handle IVF patients’ complaints.

참고문헌 (27)

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