Evidence map›Paper›PMID 35982601›Full record

ArticleHealthcare informatics research2022

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

Paminto Agung Christianto, Eko Sediyono, Irwan Sembiring

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Article in Healthcare informatics research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Paminto Agung ChristiantoDepartment of Computer Science, Satya Wacana Christian University, Salatiga, Indonesia.
Eko SediyonoDepartment of Computer Science, Satya Wacana Christian University, Salatiga, Indonesia.
Irwan SembiringDepartment of Computer Science, Satya Wacana Christian University, Salatiga, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesEighty percent of in vitro fertilization (IVF) patients have high anxiety levels, which influence the success of IVF and drive IVF patients to quickly report any abnormal symptoms. Rapid responses from fertility subspecialist doctors may reduce patients' anxiety levels, but fertility subspecialist doctors' high workload and their patients' worsening health conditions make them unable to handle IVF patients' complaints quickly. Research suggests that smart systems using case-based reasoning (CBR) can help doctors handle patients quickly. However, a prior study reported enhanced accuracy by modifying the CBR similarity formula based on Lin's similarity theory to generate the Chris case-based reasoning (CCBR) similarity formula.

methodsThe data were validated through interviews with two fertility subspecialist doctors, interviews with two IVF patients, a questionnaire administered to 17 community members, the relevant literature, and 256 records with data on IVF patients' complaints and how they were handled. An experiment compared the performance of the CBR similarity formula algorithm with the CCBR similarity formula algorithm.

resultsA confusion matrix showed that the CCBR similarity formula had an accuracy value of 52.58% and a precision value of 100%. Fertility subspecialist doctors stated that 89.69% of the CCBR similarity formula recommendations were accurate.

conclusionsWe recommend applying a combination of the CCBR 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 subspecialist doctors handle IVF patients' complaints.

Indexed as

AnxietyComplaintDoctorsFertilityPatients

Identifiers

PMID35982601
PMCPMC9388916

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