scholarly journals Accuracy Level of Diagnosis of ENT Diseases in Expert System

KOMTEKINFO ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 127-134
Author(s):  
Dede Wira Trise Putra ◽  
Ade Oka Utami ◽  
Minarni ◽  
Ganda Yoga Swara
Keyword(s):  
Expert System ◽  
Early Detection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Test Results ◽  
Forward Chaining ◽  
Accuracy Level ◽  
Nose And Throat ◽  
Delay In Treatment

Purpose of this study is to test the accuracy of ear, nose and throat (ENT) diseases with an expert system. The expert system is designed to help people make early detection of illnesses so that the possibility of delay in treatment can be minimized. The method used is Naive Bayes with Forward Chaining Inference for 14 types of diseases with 42 symptoms originating from ENT specialists. The method was tested on 25 patients who used an expert system and adjusted the results of expert diagnoses. The test results are influenced by the number of symptoms, so that the accuracy obtained is only 88%. So this research is needed to be further developed to find a more reliable expert system in diagnosing ENT diseases.

scholarly journals Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 92-98
Author(s):  
Dewi Salma Salsabila ◽  
Rinabi Tanamal
Keyword(s):  
Expert System ◽  
System Application ◽  
Digestive Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Disease Identification ◽  
Digestive Diseases ◽  
Accuracy Level ◽  
Before And After ◽  
Precise Prediction

Shown symptoms in digestive diseases might be similar, resulting in patient’s suspected diseases before and after diagnosis attempt might turn out to be different. This paper aims to build a design of an expert system for digestive disease identification using Naïve Bayes methodology for iOS-based applications. The result from this paper helps medical interns to increase the accuracy in predicting patient’s suspected digestive disease. A precise prediction in suspected disease identification can minimalize unnecessary diagnosis attempts, which saves time and reduces cost. Naïve Bayes is chosen because it has a higher accuracy level than other classification methods. This research includes collecting data through literature reviews on digestive diseases and their symptoms, processing the data to be turned into a knowledge base for the expert system, conducting data training using Naïve Bayes by the designed expert system application through this research. The result from the conducted data training using Naïve Bayes methodology shows that the expert system application has a higher accuracy level, which is 84%.

scholarly journals Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 92
Author(s):  
Dewi Salma Salsabila ◽  
Rinabi Tanamal
Keyword(s):  
Expert System ◽  
System Application ◽  
Digestive Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Disease Identification ◽  
Digestive Diseases ◽  
Accuracy Level ◽  
Before And After ◽  
Precise Prediction

Shown symptoms in digestive diseases might be similar, resulting in patient’s suspected diseases before and after diagnosis attempt might turn out to be different. This paper aims to build a design of an expert system for digestive disease identification using Naïve Bayes methodology for iOS-based applications. The result from this paper helps medical interns to increase the accuracy in predicting patient’s suspected digestive disease. A precise prediction in suspected disease identification can minimalize unnecessary diagnosis attempts, which saves time and reduces cost. Naïve Bayes is chosen because it has a higher accuracy level than other classification methods. This research includes collecting data through literature reviews on digestive diseases and their symptoms, processing the data to be turned into a knowledge base for the expert system, conducting data training using Naïve Bayes by the designed expert system application through this research. The result from the conducted data training using Naïve Bayes methodology shows that the expert system application has a higher accuracy level, which is 84%.

scholarly journals Diagnosa Penyakit Tanaman Cabai menggunakan Metode Forward Chaining dan Naïve Bayes

2021 ◽  
Vol 5 (2) ◽  
pp. 164
Author(s):  
Riyo Efendi ◽  
Fauziah Fauziah ◽  
Aris Gunaryati
Keyword(s):  
Expert System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Plant Diseases ◽  
Web Based ◽  
Bayes Methods ◽  
Forward Chaining ◽  
Speed Up ◽  
Handling Methods ◽  
The Web

The purpose of this research is to design a web-based expert system application to provide information about chili plant diseases and to diagnose the symptoms of diseases that attack chili plants and to provide solutions for appropriate handling methods, which can be accessed anywhere and utilized by the wider community and can speed up the time to deal with diseases that attack chili plants. This study uses the Forward Chaining and Naïve Bayes Methods in order to know the facts/signs of chili disease so that you can get the best quality chili. Based on the results of the discussion and calculation of the web-based expert system for diagnosing chili disease using the Forward Chaining and Naïve Bayes methods, it can be concluded that a simple application it can make it easier for users to diagnose chili disease and find out the causes and solutions. And can find out the total number of all comparisons of the 2 methods.Keywords:Chili disease, Expert System, Forward Chaining, Naïve Bayes.

scholarly journals An Expert System for the Prognostication of the Brain and Nerve Diseases in Children with Convulsion Signs Based on Certainty Factors (Preprint)

2018 ◽  
Author(s):  
Narges Roozitalab ◽  
Hamid Nemati
Keyword(s):  
Expert System ◽  
Naive Bayes ◽  
Neurological Diseases ◽  
Naïve Bayes ◽  
Rule Based ◽  
Forward Chaining ◽  
Two Systems ◽  
The One ◽  
Time Required ◽  
Expert Physician

BACKGROUND Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. OBJECTIVE Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. METHODS Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. RESULTS Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. CONCLUSIONS Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors.

scholarly journals Sistem Pakar dengan Menggunakan Metode Naive Bayes dalam Mengidentifikasi Penyakit Karies pada Gigi Manusia

2021 ◽  
pp. 233-238
Author(s):  
Tio Ramadan Sapto Hari ◽  
S Sumijan
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tooth Decay ◽  
Test Results ◽  
Accuracy Rate ◽  
Bayes Method ◽  
Human Teeth ◽  
The Public

Caries disease in human teeth is a disease that permanently destroys the inner walls of teeth and forms small holes in the teeth. The Indonesian people lack the knowledge to find information and identify tooth decay, which makes many people unaware of the consequences and dangers of this disease. Tooth decay disease is usually caused by three factors. The first factor is teeth and saliva, which are the hosts of microorganisms in the oral cavity. Bacteria and food are the second and third factors. The purpose of this research is to help the public find information about tooth decay, thus forming a branch of artificial intelligence, the expert system. Artificial intelligence is a science that allows you to build computer systems that display intelligence in different ways. An expert system is a computer program or information system that uses some knowledge of an expert. The methods used in this study are the Naive Bayes method and the model's view controller, which are implemented as a database in the PHP Codeigniter framework and MySQL. The data processed in this study is knowledge about the symptoms of dental caries obtained from experts. The test results of this method provide patients with the knowledge necessary to prevent tooth decay, with an accuracy rate of 83.61%. This expert system helps the public to recognize and obtain information about tooth decay. The Expert System can also be used to take the first step in preventing tooth decay.

scholarly journals Expert System Using Naive Bayes Method in Identifying Caries Disease in Human Teeth

2021 ◽  
pp. 231-236
Author(s):  
Tio Ramadan Sapto Hari ◽  
S Sumijan
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tooth Decay ◽  
Test Results ◽  
Bayes Method ◽  
Human Teeth ◽  
The Public ◽  
Naive Bayes Method

Caries disease in human teeth is a disease that permanently destroys the inner walls of teeth and forms small holes in the teeth. The Indonesian people lack the knowledge to find information and identify tooth decay, which makes many people unaware of the consequences and dangers of this disease. Tooth decay disease is usually caused by three factors. The first factor is teeth and saliva, which are the hosts of microorganisms in the oral cavity. Bacteria and food are the second and third factors. The purpose of this research is to help the public find information about tooth decay, thus forming a branch of artificial intelligence, the expert system. Artificial intelligence is a science that allows you to build computer systems that display intelligence in different ways. An expert system is a computer program or information system that uses some knowledge of an expert. The methods used in this study are the Naive Bayes method and the model's view controller, which are implemented as a database in the PHP Codeigniter framework and MySQL. The data processed in this study is knowledge about the symptoms of dental caries obtained from experts. The test results of this method provide patients with the knowledge necessary to prevent tooth decay, with an accuracy rate of 83.61%. This expert system helps the public to recognize and obtain information about tooth decay. The Expert System can also be used to take the first step in preventing tooth decay.

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT DAN HAMA PADA TANAMAN JAGUNG MENGGUNAKAN METODE NAIVE BAYES

2021 ◽  
Vol 2 (3) ◽  
pp. 390-398
Author(s):  
Bayu Bastiyan Suherman
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Preventive Measures ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Agricultural Commodities ◽  
Bayes Method ◽  
Mathematical Basis ◽  
Lack Of Information ◽  
Corn Plants

Corn is one of the leading agricultural commodities that can be used as a staple plant other than rice. Constraints faced by corn farmers include the lack of information about diseases that attack the corn plants, which causes less productivity. In this study a system was developed that can automatically detect disease that attacks corn plants so that preventive measures can be taken to prevent corn plants from dying. Expert systems are designed to solve certain problems by imitating the work of experts. In addition, this expert system also helps farmers who are experiencing problems regarding diseases and pests and their solutions without relying on an expert. The method used is the Naive Bayes method, Naive Bayes is a method used to predict probabilities and has several characteristics that are istitively in accordance with the way of thinking of an expert and accompanied by a strong mathematical basis. From the tests carried out with diagnoses obtained from the comparison between the results of expert diagnoses and the diagnosis of the system to diagnose diseases and pests in corn plants is 90%.

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT KULIT PADA BALITA MENGGUNAKAN METODE NAÏVE BAYES DAN FORWARD CHAINING STUDI KASUS PUSKESMAS CEMPAKA SUNGKAI SELATAN

2021 ◽  
Vol 9 (2) ◽  
pp. 144-153
Author(s):  
Sidik Rahmatullah ◽  
Rima Mawarni
Keyword(s):  
Naive Bayes ◽  
Unified Modeling Language ◽  
Naïve Bayes ◽  
Modeling Language ◽  
Class Diagram ◽  
Use Case ◽  
Activity Diagram ◽  
Unified Modeling ◽  
Forward Chaining

Pusat Kesehatan Masyarakat (Puskesmas) merupakan kesatuan organisasi fungsional yang menyelenggarakan upaya kesehatan yang bersifat menyeluruh, terpadu, merata dapat diterima dan terjangkau oleh masyarakat. Tujuan penelitian ini adalah membuat Aplikasi Sistem Pakar untuk mendeteksi penyakit kulit pada balita sesuai dengan data-data yang ada pada Puskesmas. Metode pengembangan sistem yang digunakan yaitu metode Extreme Programing (XP) dengan tahapan pengerjaan meliputi planning, design, coding dan testing. Sistem dirancang dengan menggunakan Unified Modeling Language (UML) yang meliputi use case, activity diagram, dan class diagram, perangkat lunak yang digunakan adalah PHP (Hypertext Preprocessor) dengan database MySQL dan menggunakan metode Naïve Bayes dan Forward Chaining. Hasil akhir dari pembuatan Aplikasi ini adalah memudahkan pasien, dokter dan bidan mendeteksi penyakit kulit pada balita di Puskesmas

scholarly journals Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

2021 ◽  
Vol 6 (3) ◽  
pp. 178-188
Author(s):  
Adhitya Prayoga Permana ◽  
Kurniyatul Ainiyah ◽  
Khadijah Fahmi Hayati Holle
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Test Results ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Start Up ◽  
The Many ◽  
Start Ups

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

scholarly journals Sistem Pakar Diagnosis Penyakit Tanaman Padi Menggunakan Forward Chaining dan Dempster Shafer

2020 ◽  
Vol 2 (2) ◽  
pp. 248-257
Author(s):  
Dianmita Ayu Putri ◽  
Arik Aranta
Keyword(s):  
Expert System ◽  
Theoretical Calculation ◽  
Rice Plant ◽  
Crop Production ◽  
Black Box ◽  
Plant Diseases ◽  
Test Results ◽  
Forward Chaining ◽  
Opinion Score ◽  
Black Box Testing

Rice (Oryza sativa) is one of the main commodities in Indonesia. One of the inhibitors of rice crop production is disease. Rice plant diseases can be caused by pathogens, host plants or bad environment. The process of diagnosing rice diseases requires expertise, knowledge and experience. This study aims to build an expert system that can diagnose 13 types of rice plant diseases from 43 symptoms based on the knowledge of 3 experts with forward chaining reasoning methods and mobile-based dempster shafer calculation methods. The testing technique used is black box testing, theoretical calculation testing, system accuracy testing and MOS (Mean Opinion Score) testing. The black box test results state that the expert system has 100% compatibility in terms of functionality. The theoretical calculation test results state that the expert system calculations are in accordance with the results of manual calculations. System accuracy testing results from 30 test cases get an accuracy of 81.11%. The results of MOS testing conducted on 30 respondents produced MOS of 4.2 from a scale of 5 categorized into a good system.

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