scholarly journals ANALISIS PERBANDINGAN TIGA METODE UNTUK MENDIAGNOSA PENYAKIT MATA PADA MANUSIA

2021 ◽  
Vol 8 (4) ◽  
pp. 1654-1664
Author(s):  
Ahmad Fahmi Adam
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Certainty Factor ◽  
Case Based

Untuk mendiagnosa penyakit mata pada manusia diperlukan perhitungan probabilitas yang terbaik. Karena mata merupakan salah satu bagian terpenting pada tubuh manusia yang harus di jaga kesehatannya. Penelitian ini bertujuan untuk menganalisis perbandingan dari 3 metode diantaranya : metode Case-Based Reasoning, Naïve Bayes dan Certainty Factor sehingga bisa diketahui metode mana yang terbaik untuk melakukan pendiagnosaan. Setelah melakukan perbandingan, untuk perhitungan metode Case-Based Reasoning didapatkan hasil probabilitas 61,6 %, metode Naïve Bayes didapatkan hasil 56,36% dan metode Certainty Factor didapatkan hasil 90,4%. Dapat disimpulkan, metode Certainty Factor adalah metode yang terbaik untuk melakukan pendiagnosaan penyakit mata pada manusia. Setelah itu, akan dibuatkan suatu sistem pakar menggunakan metode Certainty Factor untuk mendiagnosa penyakit mata pada manusia. Sistem pakar merupakan peniru suatu pakar dalam melakukan diagnosis suatu penyakit. Tujuan dibuatkan sistem pakar ini, supaya dapat membantu pasien untuk mendiagnosa jenis penyakit mata apa berdasarkan gejala gejala yang dialaminya.

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases Abstract (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

scholarly journals PENENTUAN KESESUAIAN LAHAN PERTANIAN TANAMAN CABAI MENGGUNAKAN METODE NAÏVE BAYES DI KABUPATEN KUPANG

2021 ◽  
Vol 9 (1) ◽  
pp. 56-64
Author(s):  
Welmy Sinlae ◽  
Sebastianus A. S. Mola ◽  
Nelci Dessy Rumlaklak
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Case Based

Tanaman cabai merupakan salah satu tanaman yang dibudidayakan di Nusa Tenggara Timur (NTT). Wilayah Kabupaten Kupang merupakan salah satu wilayah penghasil cabai yang ada di NTT. Produksi cabai secara keseluruhan di Kabupaten Kupang pada tahun 2019 sampai 2020 mengalami peningkatan. Namun peningkatan produksi ini belum maksimal mengingat banyaknya lahan yang belum dimanfaatkan sebagai lahan pertanian. Oleh karena itu dibutuhkan sebuah sistem yang membantu dalam menentukan kesesuaian lahan pertanian untuk penanaman cabai. Dalam penelitian ini penulis menerapkan penalaran berbasis kasus/case-based reasoning (CBR) dalam menentukan kesesuaian lahan pertanian tanaman cabai. Metode yang digunakan dalam penelitian ini adalah Naïve Bayes dengan 7 kriteria yaitu: curah hujan, drainase, tekstur tanah, kedalaman tanah, C-organik, kemiringan lahan dan bahaya terjadinya bencana. Proses pencarian solusi dimulai dengan mengeliminasi data yang tidak relevan menggunakan metode Naive Bayes dan berlanjut dengan perankingan nilai kemiripan terbaik menggunakan KNN. Berdasarkan hasil pengujian dengan 110 kasus lahan cabai didapatkan hasil akurasi tertinggi sebesar 92.2% dan rata-rata hasil akurasi dari keseluruhan fold sebesar 89.1%.      

Case Based Reasoning And Naive Bayes Implementation In Laptop Purchasing Recommender System

2021 ◽  
Vol 9 (3) ◽  
pp. 411
Author(s):  
I Gusti Ngurah Agung Dharmawangsa ◽  
I Wayan Supriana
Keyword(s):  
Recommender System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
The Right ◽  
Case Based ◽  
Selection Of ◽  
The Ideal

Purchasing a new laptop will be difficult if we do not know what the ideal laptop specification for our needs. Especially with a wide selection of laptops. From this problem, system that can give a recommendation to choose the right laptop based on purchaser’s specification choice is needed. This research using two method, Case Based Reasoning and Naive Bayes. The concept of Case Based Reasoning is the process of solving new problems based on the solutions of similar past problems, While Naive Bayes assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive bayes will be implemented in retrive process of case based reasoning. The recommender system utilizing 7 feature, Kecepatan Processor, Kapasitas Ram, Tipe Grafis, Ukuran Layar, Ukuran Harddisk, Kecepatan Layar, and Harga. The percentage of respondents who said the system was successful in providing the right recommendations was 70 percent of the total respondents.

scholarly journals Development of Android-based Rabbit Disease Expert System

2018 ◽  
Vol 7 (4.44) ◽  
pp. 82
Author(s):  
Dyah Ayu Irawati ◽  
Yan Watequlis Syaifudin ◽  
Fabiola Ester Tomasila ◽  
Awan Setiawan ◽  
Erfan Rohadi
Keyword(s):  
Naive Bayes ◽  
Diagnostic System ◽  
Naïve Bayes ◽  
Certainty Factor ◽  
Bayes Method ◽  
Trust Value ◽  
User Input ◽  
Sample Data ◽  
Testing Data ◽  
Naive Bayes Method

Many rabbit keepers or breeders are panics when their rabbit has an illness. This paper proposed an expert diagnostic system application for Android-based rabbit disease using the Naïve Bayes method to determine the illness and Certainty Factor for the trust value of the condition by combining the rate of the trust of users and experts due to diagnose the diseases of the rabbit.The testing was using 65 data learning and 160 data learning to test the naïve Bayes method. Furthermore, the certainty factor is using CF user 1 and its variation.The results obtained for 65 data learning is 53%, while 160 data learning is 73%. With the naïve Bayes method, it can be concluded that the more data learning, the better and more accurate the system. The results of conformity with the testing data obtained from the variative CF user value, namely 53% accordingly, 13% inappropriate, 33% near. The effect of compliance with the sample data collected from the CF value of user 1 is 53% appropriate, 7% inappropriate, 40% is near. With the certainty factor method, it can be concluded that differences in user input values affect the overall CF value. 

scholarly journals Perbandingan Naïve bayes dan Certainty factor pada Sistem Pakar Untuk Mendiagnosa Dini Penyakit Glaukoma

2020 ◽  
Vol 5 (3) ◽  
pp. 291
Author(s):  
Hanif Rahman Burhani ◽  
Iskandar Fitri ◽  
Andrianingsih Andrianingsih
Keyword(s):  
Expert System ◽  
Naive Bayes ◽  
Comparison Method ◽  
Naïve Bayes ◽  
Training Data ◽  
Certainty Factor ◽  
Bayes Method ◽  
Lack Of Information ◽  
Statistics And Probability ◽  
Naive Bayes Method

Glaucoma is an eye disease that causes the second largest blindness after cataracts, this disease can cause decreased vision and can even be fatal, namely permanent blindness if it is not realized and treated immediately. Lack of information and education to the public to always maintain eye health is the basis for the purpose of making this expert system which aims to provide early diagnosis to people who are indicated to have glaucoma based on the symptoms or characteristics previously felt. The Naïve bayes method is a method that uses statistics and probability in predicting a person's chance of suffering from glaucoma based on the symptoms previously felt. It is made based on a website with PHP as the programming language and uses MySQL for the database. As for the comparison method used is the Certainty factor, which is a method that functions to determine a certainty value based on the calculation of the predetermined CF value by applying manual calculations. In the Naïve bayes method, the application can group symptom data and types of disease and can diagnose based on previous training data, while for the Certainty factor method based on the calculation of the value of the expert and the CF value that has been inputted by the user, it can produce a percentage of the diagnosis of the disease glaucoma in 96%.Keywords:Certainty factor, Expert System, Glaucoma, MySQL, Naïve bayes, PHP.

scholarly journals Comparison between Naïve Bayes and certainty factor to predict big five personality

2019 ◽  
Vol 1402 ◽  
pp. 077030
Author(s):  
U Syaripudin ◽  
R Zaenal ◽  
M F A Duri ◽  
E Firmansyah ◽  
A Rahman
Keyword(s):  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Big Five Personality ◽  
Certainty Factor

scholarly journals Penentuan Ekstrakurikuler Siswa Sesuai Minat Bakat dengan Case-Based Reasoning dan Certainty Factor

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Arjun Sirojul Anam ◽  
Faris Muslihul Amin ◽  
Mujib Ridwan
Keyword(s):  
Extracurricular Activities ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Case Based Reasoning ◽  
Well Testing ◽  
Certainty Factor ◽  
Web Based ◽  
Precision Level ◽  
Web Based System ◽  
Case Based

Extracurricular activities at MAN 1 Lamongan are still determined without any support from the system. Students are only given extracurricular information and can register according to the conditions if interested. This makes the extracurricular that students have chosen does not fully match their abilities. The result is a decrease in the number of members who are active in extracurricular activities due to loss of interest. A web-based system was developed to assist MAN 1 Lamongan in determining extracurricular according to interests and talents. Case-Based Reasoning (CBR) is the system framework and Certainty Factor (CF) is the algorithm for determining the certainty value. The result is that with test data of 68 students, the system recommends extracurricular well. Testing with Confusion Matrix obtained precision level of 96.03% (high), recall of 99.4% (high), accuracy of 95.76% (high)

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