scholarly journals Case-based Reasoning for Skin Diseases Diagnose using Minkowski Distance

2019 ◽  
Author(s):  
. Mihuandayani ◽  
Yufika Sari Bagi ◽  
Theofani Christi Irene Momongan
Keyword(s):  
Skin Diseases ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Case Based

Decision Support Techniques for Dermatology Using Case-Based Reasoning

2020 ◽  
Vol 20 (03) ◽  
pp. 2050024
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh ◽  
P. Johnson Durai Raj
Keyword(s):  
Decision Support ◽  
Skin Diseases ◽  
Treatment Plan ◽  
Empirical Evaluation ◽  
Decision Support Techniques ◽  
Skin Lesions ◽  
Data Repository ◽  
Case Based Reasoning ◽  
Hybrid Architecture ◽  
Case Based

Identification of skin disease has become a challenging task with the origination of various skin diseases. This paper presents a case-based reasoning (CBR) decision support system to enhance dermatological diagnosis for rural and remote communities. In this proposed work, an automated way is introduced to deal with the inconsistency problem in CBRs. This new hybrid architecture is to support the diagnosis in multiple skin diseases. The architecture used case-based reasoning terminology facilitates the medical diagnosis. Case based reasoning system retrieves the data which contains symptoms and treatment plan of the disease from the data repository by the way of matching visual contents of the image, such as shape, texture, and color descriptors. The extracted feature vector is fed into a framework to retrieve the data. The results proved using ROC curve that the proposed architecture yields high contribution to the computer-aided diagnosis of skin lesions. In experimental analysis, the system yields a specificity of 95.25% and a sensitivity of 86.77%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works.

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 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 DIAGNOSIS PENYAKIT HIPERTENSI MENGGUNAKAN METODE CASE-BASED REASONING

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Amalia Beladinna Arifa ◽  
Gita Fadila Fitriana
Keyword(s):  
Cross Validation ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Fold Cross Validation ◽  
Case Based

Hipertensi adalah kondisi ketika tekanan darah pada pembuluh darah bersih meningkat secara kronik. Jika tidak segera ditangani dapat menyebabkan peningkatan resiko kejadian penyakit lainnya, misalnya kardiovaskuler, serebrovaskuler dan renovaskuler. Diagnosis penyakit hipertensi perlu ditegakkan sedini mungkin guna menurunkan peningkatan resiko kejadian penyakit lainnya. Penelitian ini bertujuan menghasilkan sistem yang mengimplementasikan metode Case-Based Reasoning yang dapat membantu paramedis untuk mendiagnosis penyakit hipertensi. Implementasi sistem dirancang menggunakan bahasa pemrograman PHP serta penyimpanan data kasus menggunakan MySQL. Kasus-kasus penyakit hipertensi yang sudah berhasil ditangani oleh dokter dijadikan sebagai data acuan untuk mendiagnosis kasus hipertensi yang baru. Kasus baru yang berisi faktor resiko, gejala dan riwayat penyakit selanjutnya dicari kemiripannya dengan kasus-kasus lama dengan cara menghitung nilai similaritas menggunakan Minkowski Distance. Pengujian dilakukan pada 172 data menggunakan 10-fold Cross-Validation. Hasil perhitungan dengan menetapkan threshold sebesar 0,90 didapatkan tingkat akurasi sebesar 94,71%. Hasil penelitian menunjukkan implementasi Case-Based Reasoning dapat digunakan untuk melakukan diagnosis penyakit hipertensi

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 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 Sistem Pakar Diagnosa Penyakit Kulit Anjing Menggunakan Metode Case Based Reasoning dan Algoritma K-Nearest Neighbour

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ida Bagus Yoga Semara Putra ◽  
Setiawan Wibisono
Keyword(s):  
Expert System ◽  
Skin Diseases ◽  
Case Based Reasoning ◽  
Nearest Neighbour ◽  
Knowledge Based ◽  
Treatment Information ◽  
Sample Data ◽  
Weight Value ◽  
The Right ◽  
Case Based

Abstract—Dogs are one of the favorite animals that are commonly maintained by humans. Like humans, dogs can also be affected by illness. One common illness suffered by dogs is skin disease. Dog skin diseases that are not immediately handled properly can worsened his condition and can pass it on to other animals even humans. To minimize the problem, we create an expert system to diagnose skin diseases in dogs. In order to support the process, the expert system uses Case Based Reasoning (CBR) method with the algorithm K-Nearest Neighbour (K-NN). The application of K-NN algorithm on CBR's knowledge-based system can provide quick and practical diagnostic results and provide the right advice to the sufferer to obtain alternative treatment information that suits the type of disease. The weight value of the CBR method gained from the classification between symptoms is heavy, moderate, and mild. The appearance of the weight value of each symptom of each disease in the case sample data will be used in the calculation process to obtain the result of a percentage of skin diseases in dogs. The results obtained from this research are the types of diseases suffered and the solution of the disease. Based on system testing of 12 disease data with 27 symptom data obtained at 100% accuracy.

scholarly journals Diagnosa Penyakit Kulit Menggunakan Case Based-Reasoning dan Self Organizing Maps

2020 ◽  
Vol 6 (1) ◽  
pp. 53
Author(s):  
Fhatiah Adiba ◽  
Nurul Mukhlisah Abdal ◽  
Andi Akram Nur Risal
Keyword(s):  
Nearest Neighbor ◽  
Skin Diseases ◽  
Case Based Reasoning ◽  
Self Organizing Maps ◽  
Indexing Method ◽  
Case Based ◽  
Self Organizing

This study aims to compare the results of the accuracy and speed of the system in diagnosing skin diseases using the case based reasoning (CBR) method with the indexing method and without using indexing. Self-organizing maps (SOM) are used as an indexing method and the process of finding similarity values uses the nearest neighbor method. Testing is done with two scenarios. The first scenario uses CBR without indexing self-organizing maps, the second scenario uses CBR with indexing self-organizing maps. The accuracy of the diagnosis of skin diseases at a threshold ≥80 for CBR without indexing self-organizing maps is 93.46% with an average retrieve time of 0.469 seconds while CBR testing using SOM indexing is 92.52% with an average retrieve time of 0.155 seconds. The results of comparison of CBR methods without using show higher results than using SOM indexing, but the process of retrieving CBR using SOM is faster than not using indexing

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