Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

Takkar, Sakshi

doi:10.23940/ijpe.17.04.p2.348361

Cited by 5 publications

(6 citation statements)

References 36 publications

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“…The confusion matrix showed that the accuracy of the CCBR similarity formula was better than that of the CBR similarity formula. The low accuracy value of CBR (19.59%) is similar to that of 17.32% reported in previous research [ 25 ]. The difference in the accuracy value between the CCBR similarity formula and the CBR similarity formula of 32.99% also corresponds to reported in previous research of 35%, with CBR showing lower accuracy [ 26 ].…”

Section: Discussionsupporting

confidence: 89%

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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

Christianto¹,

Sediyono

Sembiring

2022

Healthc Inform Res

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Objectives: Eighty percent of <i>in vitro</i> 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. Methods: The 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. Results: A 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. Conclusions: We 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.

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Section: Discussionsupporting

confidence: 89%

Section: Accuracy Of the Similarity Value Of The Cbr Similaritysupporting

confidence: 89%

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

Christianto¹,

Sediyono

Sembiring

2022

Healthc Inform Res

View full text Add to dashboard Cite

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“…The precision achieved in this recently referenced effort is less. [15] prescribed another idea that combined both Case-Based Reasoning (CBR) theory strengthened by Genetic Algorithm (GA) to streamline the desire results of liver ailment. The heaps were assigned to the features according to their belongings for causing liver illness.…”

Section: Related Workmentioning

confidence: 99%

Liver Disease Classification using Deep Learning Algorithm

Anand¹,

Neelanarayanan²

2019

IJITEE

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Data Mining is one of the prevalent elucidating portions of programmed request and distinguishing proof. It involves data mining counts and strategies to examine helpful data. Of late, liver dissents have disproportionately expanded and liver infections are complimenting one of the most human pains in different countries. Early assurance of Liver Disorder is essential for the welfare of human culture. This complaint should be considered sincerely by setting up watchful structures for the early break down and expectation of Liver contaminations. The robotized gathering system suffers with non attendance of precision results when differentiated and cautious biopsy. We propose another model for liver issue request for separating the patient's helpful, data using ANN algorithm. The remedial records are organized whether there is a believability of essence of disorder or not. This proposed methodology uses extracted features using M-PSO and ANN for classifying the features. The ANN methodology improves the accuracy when appeared differently in relation to existing request computations. This paper focuses classification of selected features for classification.

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“…The exactness accomplished in this previously mentioned exertion is less. [15] recommended another thought that consolidated both Case-Based Reasoning (CBR) philosophy fortified by Genetic Algorithm (GA) to streamline the expectation consequences of liver sickness. The loads were allocated to the highlights as per their effects for causing liver malady.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm

2019

IJRTE

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Information Mining is one of the preeminent descriptive parts of automatic order and identification. It embroils information mining calculations and methods to inspect restorative information. Lately, liver protests have lopsidedly increased and liver diseases are complimenting one of the most human afflictions in various nations. Early determination of Liver Disorder is basic for the welfare of human culture. This grievance ought to be considered earnestly by setting up canny frameworks for the early analyze and anticipation of Liver infections. The robotized grouping framework endures with absence of exactness results when contrasted and careful biopsy. We propose another model for liver disorder order for breaking down the patient's restorative information utilizing cross breed counterfeit neural system. The therapeutic records are arranged whether there is a plausibility of presence of sickness or not. This proposed strategy utilizes M-PSO for highlight choice of information factors and M-ANN calculation for sickness order. The exhibited crossover approach improves the precision when contrasted with existing order calculations. This paper concentrates on feature selection on using PSO algorithm

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Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

Cited by 5 publications

References 36 publications

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

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

Liver Disease Classification using Deep Learning Algorithm

Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm

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