scholarly journals Case-Based Reasoning untuk Diagnosis Penyakit Jantung

2017 ◽  
Vol 11 (1) ◽  
pp. 1 ◽  
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%

scholarly journals Case Based Reasoning untuk Diagnosis Penyakit Jantung Menggunakan Metode Minkowski Distance

2018 ◽  
Vol 1 (1) ◽  
pp. 119-123
Author(s):  
Eka Wahyudi ◽  
Novi Indah Pradasari
Keyword(s):  
Heart Disease ◽  
Computer System ◽  
Medical Records ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Test Results ◽  
Minkowski Distance ◽  
Heart Disease Diagnosis ◽  
System Knowledge ◽  
Case Based

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.

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

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.

scholarly journals Penyelesaian Masalah Pengelolaan Lumbung Pangan Desa Menggunakan Case-Based Reasoning dengan Algoritma K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mgs. Afriyan Firdaus ◽  
Dwi Rosa Indah ◽  
Putri Eka Sevtiyuni ◽  
Choirunnisa Qonitah
Keyword(s):  
Problem Solving ◽  
Nearest Neighbor ◽  
Technical Problem ◽  
Case Based Reasoning ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Existing Problems ◽  
K Nearest Neighbor Algorithm ◽  
Case Based

In this paper, we discuss the problem solving of village food barn management using Case-Based Reasoning (CBR) with the K-Nearest Neighbor algorithm. This research was carried out by adopting the stages of the CBR cycle and the nearest neighbor algorithm. The results of the study show that the application of CBR and K-nearest neighbor algorithms can support the resolution of knowledge problems in village food barn management using technical problem solving based on the symptoms and solutions to existing problems. Based on the test results, the problem-solving accuracy was 92%.Keywords - case-based reasoning, K-nearest neighbor, food barn, problem-solving

scholarly journals SISTEM PENALARAN BERBASIS KASUS UNTUK PENDUKUNG DIAGNOSIS GANGGUAN PENYAKIT PADA UNGGAS (Case Based Reasoning System to Support Diagnosis of Diseases in Poultry)

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Murien Nugraheni ◽  
Sri Hartati
Keyword(s):  
Cosine Similarity ◽  
Case Based Reasoning ◽  
Test Cases ◽  
Test Results ◽  
Reasoning System ◽  
C4.5 Algorithm ◽  
Amount Of Knowledge ◽  
Similarity Method ◽  
Case Based ◽  
Diagnosis Of Diseases

Case-Based Reasoning (CBR) is a computer system that uses old knowledge to solve new problems. CBR provide solutions for new cases by looking at an old case that comes closest to the new case. It will be very useful because it eliminates the need to extract the model as required by the rules-based system. Moreover, CBR can also be started from a small amount of knowledge, because the knowledge of CBR can be increased gradually when a case is added.This study tries to establish a system for Case-Based Reasoning System to Support Diagnosis of Diseases in Poultry by looking at the characteristics of existing symptoms in poultry. Diagnosis process is done by inserting a new cases that contain the symptoms of the disease to be diagnosed into the system, then the system will do the indexing process or classification with C4.5 algorithm method to obtain an index of new cases. After obtaining an index of the cases, then the system do the calculating of the value of similarity between the new case by case which has the same index with new cases using Cosine Similarity method. The case taken is the case with the highest similarity value. If a case is not successfully diagnosed, then the case will be revised by experts. Revised successful cases will be stored into the system to be used as new knowledge for the system.The results showed case-based reasoning system to diagnose disease of poultry can help experts and farmers in performing diagnostics. The test results of 30 test cases, system has been to produce similarity of 28 cases (93.33%) and obtained 2 cases (6.67%) have similarity values below 0.8 will be revised by experts.Keywords: CBR, poultry, indexing, similarity, cosine similarity

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

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.

scholarly journals New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

2019 ◽  
Vol 10 (3) ◽  
pp. 667-678 ◽  
Author(s):  
Jalil Nourmohammadi-Khiarak ◽  
Mohammad-Reza Feizi-Derakhshi ◽  
Khadijeh Behrouzi ◽  
Samaneh Mazaheri ◽  
Yashar Zamani-Harghalani ◽  
...  
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Nearest Neighbor ◽  
Imperialist Competitive Algorithm ◽  
Disease Diagnosis ◽  
K Nearest Neighbor ◽  
Feature Selection Technique ◽  
Medical Databases ◽  
Classification Evaluation ◽  
Heart Disease Diagnosis

AbstractThe number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

scholarly journals Case Base Reasoning Untuk Mendiagnosis Penyakit Hipertensi Menggunakan Metode Indexing Density Based Spatial Clustering Application With Noise (DBSCAN)

2019 ◽  
Vol 7 (1) ◽  
pp. 88-100
Author(s):  
Herdiesel Santoso
Keyword(s):  
Euclidean Distance ◽  
Spatial Clustering ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Unhealthy Lifestyle ◽  
Distance Method ◽  
Threshold Score ◽  
Case Base Reasoning ◽  
Minimum Points ◽  
Case Based

Abstract. Hypertension is one of the health problems priority in the world because of the increasing of life expectancy and an unhealthy lifestyle. Many people with hypertension are unreachable and undiagnosed by a health worker and they do not do treatment according to the health recommendation. The Case-Based Reasoning (CBR) Method can be applied to solve the new cases in diagnosed hypertension using the answer or experience from the old case by comparing the new case and the old case. In order to do not use all the basic case data for finding a similar case, it makes an indexing process is needed. The DBSCAN algorithm implementation as indexing method is expected to improve the time and memory efficiency in CBR, especially during the retrieval process. The result of the CBR test with the cluster-indexing has a better accuracy and time process than the non-indexing CBR. The minimum parameter points and epsilon that has been chosen for clustering on hypertension data case is the combination of epsilon score 9 and minimum points score 3 with the silhoutte coefficient score 0.240 and average cluster time 0.541 seconds. The Minkowski Distance method has better accuracy than the Euclidean Distance method because by the threshold score ≥ 0.9 the CBR system with the Minkowski distance method is able to diagnose the disease with 100 % accuracy and the average best retrieval time, it is 0.0586 second Abstrak. Hipertensi menjadi salah satu prioritas masalah kesehatan di dunia karena peningkatan angka harapan hidup dan gaya hidup yang tidak sehat. Banyak penderita hipertensi yang tidak terjangkau dan terdiagnosis oleh tenaga kesehatan serta tidak menjalani pengobatan sesuai anjuran kesehatan. Metode Case-Based Reasoning (CBR) dapat diaplikasikan untuk menyelesaikan masalah baru dalam diagnosis penyakit hipertensi menggunakan jawaban atau pengalaman dari masalah lama  dengan membandingkan kasus baru dengan kasus lama. Supaya proses pencarian kasus yang mirip tidak perlu melibatkan seluruh data pada basis kasus,maka diperlukan proses indexing. Implementasi algoritme DBSCAN sebagai metode indexing diharapkan dapat meningkatkan efisiensi waktu dan memori pada CBR khususnya ketika proses retrival. Hasil pengujian CBR dengan cluster-indexing memiliki akurasi dan waktu proses yang lebih baik dari pada CBR non-indexing. Parameter minimum points dan epsilon yang dipilih untuk melakukan clustering pada data kasus penyakit hipertensi adalah kombinasi epsilon 9 dan minimum points 3 dengan nilai silhoutte coeffisien 0.240 dan waktu klaster rata-rata 0.541 detik. Metode minkowski distance memiliki akurasi yang lebih baik dari pada metode euclidean distance, karena dengan threshold ≥ 0.9 sistem CBR dengan metode minkowski distance mampu mendignosis penyakit dengan akurasi 100% dan waktu retrieve rata-rata terbaik yaitu 0.0586 detik.

scholarly journals Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder

2021 ◽  
Vol 5 (1) ◽  
pp. 23
Author(s):  
Anna Hendri Soleliza Jones ◽  
Cicin Hardiyanti
Keyword(s):  
Personality Disorder ◽  
Personality Disorders ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
World Health ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Professional Psychologists ◽  
Case Data ◽  
Case Based

A personality disorder is a condition of a person with an extreme personality that causes the sufferer to have unhealthy and different thoughts patterns and behavior from other people. The personality disorders discussed in this study consisted of 110 diseases with 300 case data and 68 symptoms. Based on Basic Health Research (Riskesdas) 2018 data, it shows that more than 19 million people aged 15 years and over were affected by mental-emotional disorders. Data from the Statistics Indonesia in 2019 that the population of Indonesia is around 265 million people, while according to the Indonesian Clinical Psychologist Association, the number of verified professional psychologists is 1,599 clinical psychologists out of a total membership of 2,078 as of January 2019. However, this figure does not meet the standards of the World Health Organization (WHO), which is that psychologists serve 30 thousand people. This shows that Indonesia still lacks around 28,970 psychologists. The unequal distribution of professional psychologists has made psychologists need a long time to provide a diagnosis because of the number of patients being inversely proportional to the availability of psychologists in Indonesia. Moreover, there is not enough patient knowledge about the symptoms they feel. This study aims to produce a system for diagnosing personality disorders. This study is a case based reasoning to solve problems that have occurred in previous cases using K-Nearest Neighbor to classify data based on the closest distance using the calculation of the Euclidean Distance. Algorithm testing for the system used the Confusion Matrix test. Based on the results of testing data in the 60 case data using K-nearest Neighbor and the calculation of the Euclidean Distance with a score of K=3, it is known that 60 data have 100% similarity to cases with a personality disorder. Meanwhile, testing new cases with 10 case data that were not in the knowledge base was also conducted showing that 9 cases had 100% similarity to the previous case, while another case had 90% similarity to the previous case.

scholarly journals Case Based Reasoning untuk Diagnosis Penyakit Ikan Kakap Putih

2020 ◽  
Vol 9 (1) ◽  
pp. 45-50
Author(s):  
Nurfalinda ◽  
Alena Uperati
Keyword(s):  
Bayesian Model ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Base System ◽  
Testing Technique ◽  
Indexing Method ◽  
Testing Techniques ◽  
Case Base ◽  
Similarity Method ◽  
Case Based

Case Based Reasoning (CBR) is one reasoning from an expert system, namely by reasoning from previous cases that have been stored on a case base to find out the solution of a new case. In case based reasoning there is a retrive process, in the retrieve process there is a similarity process, and to speed up the retrieve process it can use the indexing method. In this research will use the indexing method with Bayesian models and similarity processes using the nearest neighbor method. System testing techniques from this study with two testing techniques namely: the first testing technique using the Bayesian indexing model, the results of the indexing have produced white snapper disease, then proceed with similarity method with the nearest neighbor method used to determine the right solution from the previous case. has been saved on a case base. The second testing technique is without using indexing, the process is only by the nearest neighbor similarity method, the results of similarity in the form of disease and treatment solutions from previous cases that have been stored on a case base. System accuracy for testing with Bayesian model indexing and nearest neighbor similarity with threshold 0,70 is 86% and testing without indexing with Bayesian model with threshold 0,70 is 100%.

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