Case Based Reasoning untuk Diagnosis Penyakit Jantung Menggunakan Metode Minkowski Distance

Case Based Reasoning is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR for diagnosing heart disease. The diagnosis process is done by inserting new cases containing symptoms into the system, then the similarity value calculation between cases uses the minkowski distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold less than 0.80, the case will be revised by experts. Revised successful cases are stored to add the system knowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using minskowski distance similarity correctly of 100 percent. Using minkowski get accuracy of 100 percent. Keywords : Case Based Reasoning, Minkowski Distance Similarity.

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Case-Based Reasoning untuk Diagnosis Penyakit Jantung

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.15523 ◽

2017 ◽

Vol 11 (1) ◽

pp. 1 ◽

Cited By ~ 4

Author(s):

Eka Wahyudi ◽

Sri Hartati

Keyword(s):

Heart Disease ◽

Euclidean Distance ◽

Nearest Neighbor ◽

Disease Diagnosis ◽

Case Based Reasoning ◽

Test Results ◽

Minkowski Distance ◽

Heart Disease Diagnosis ◽

Similarity Method ◽

Case Based

Case Based Reasoning (CBR) is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR for diagnosing heart disease. The diagnosis process is done by inserting new cases containing symptoms into the system, then the similarity value calculation between cases uses the nearest neighbor method similarity, minkowski distance similarity and euclidean distance similarity. Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold <0.80, the case will be revised by experts. Revised successful cases are stored to add the systemknowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis. The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%

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Clinical Decision Support Model of Heart Disease Diagnosis Based on Bayesian Networks and Case-Based Reasoning

The 19th International Conference on Industrial Engineering and Engineering Management ◽

10.1007/978-3-642-38391-5_23 ◽

2013 ◽

pp. 219-225 ◽

Cited By ~ 3

Author(s):

Man Xu ◽

Jiang Shen

Keyword(s):

Heart Disease ◽

Decision Support ◽

Bayesian Networks ◽

Clinical Decision Support ◽

Clinical Decision ◽

Disease Diagnosis ◽

Case Based Reasoning ◽

Decision Support Model ◽

Heart Disease Diagnosis ◽

Case Based

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DESIGN AND IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED HEART DISEASE DIAGNOSIS SYSTEM

Indonesian Journal of Engineering and Science ◽

10.51630/ijes.v3i1.33 ◽

2021 ◽

Vol 3 (1) ◽

pp. 033-040

Author(s):

I Putu Agus Eka Pratama

Keyword(s):

Artificial Intelligence ◽

Heart Disease ◽

Design Science ◽

Diagnostic System ◽

Science Research ◽

Disease Diagnosis ◽

Case Based Reasoning ◽

Diagnosis System ◽

Design And Implementation ◽

Heart Disease Diagnosis

As one of the deadliest diseases in the world, heart disease requires serious treatment. The weaknesses of providing services for heart disease in Bali Province are that there is no online diagnostic system to make it easier for people to check their health conditions to find out whether they have heart disease. Based on this research, the design and implementation of a web-based online heart disease diagnosis system are carried out. The diagnostic system uses Artificial Intelligence and inputs data from the user based on several questions posed by the system. This research uses Case-Based Reasoning (CBR) algorithm with Design Science Research Methodology (DSRM) and a case study qualitative research method. The test results show that the system designed and implemented can run well and perform accurate diagnostics according to the design and user needs.

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Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.30423 ◽

2018 ◽

Vol 12 (2) ◽

pp. 139

Author(s):

Damar Riyadi ◽

Aina Musdholifah

Keyword(s):

Heart Disease ◽

Thyroid Disease ◽

Euclidean Distance ◽

Manhattan Distance ◽

Case Based Reasoning ◽

Minkowski Distance ◽

Retrieval Time ◽

Accuracy Comparison ◽

Speed Up ◽

Case Based

This study aims to improve the performance of Case-Based Reasoning by utilizing cluster analysis which is used as an indexing method to speed up case retrieval in CBR. The clustering method uses Local Triangular Kernel-based Clustering (LTKC). The cosine coefficient method is used for finding the relevant cluster while similarity value is calculated using Manhattan distance, Euclidean distance, and Minkowski distance. Results of those methods will be compared to find which method gives the best result. This study uses three test data: malnutrition disease, heart disease, and thyroid disease. Test results showed that CBR with LTKC-indexing has better accuracy and processing time than CBR without indexing. The best accuracy on threshold 0.9 of malnutrition disease, obtained using the Euclidean distance which produces 100% accuracy and 0.0722 seconds average retrieval time. The best accuracy on threshold 0.9 of heart disease, obtained using the Minkowski distance which produces 95% accuracy and 0.1785 seconds average retrieval time. The best accuracy on threshold 0.9 of thyroid disease, obtained using the Minkowski distance which produces 92.52% accuracy and 0.3045 average retrieval time. The accuracy comparison of CBR with SOM-indexing, DBSCAN-indexing, and LTKC-indexing for malnutrition diseases and heart disease resulted that they have almost equal accuracy.

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