Expert System For Disease Diagnosis Research Articles

A clinical expert system for Noma disease diagnosis

A clinical expert system for Noma disease diagnosis

ESforRPD2: Expert System for Rice Plant Disease Diagnosis.

One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. The system can also provide recommendations for the treatment of identified diseases. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id.

Web-based skin disease diagnosis expert system

. In tropical nations, such as Indonesia, skin diseases are a common ailment. Skin conditions are typically not lethal illnesses, thus patients frequently disregard them and don't take them seriously. Skin conditions can, however, become uncomfortable and threaten the sufferer's survival if they are left untreated for an extended period of time. The result of this research is the creation of a web-based expert system for identifying skin disorders, which can aid in more accurate and timely diagnosis of skin diseases as well as offer treatment advice. The development of a web-based expert system for diagnosing skin diseases uses the forward chaining method. While the development methodology uses ESDLC (Expert System Development Life Cycle), with the phases of Assessment, Knowledge Acquisition, Design, Test, Documentation, and Maintenance

Sistem Pakar Diagnosa Penyakit pada Tanaman Jambu Kristal Menggunakan Metode Dempster Shafer Berbasis Web

The Crystal Guava plant can be attacked by several types of diseases. Diseases in crystal guava plants can cause significant losses in fruit production. Some pests and diseases that can attack crystal guava plants include fruit flies, bagworms, whiteflies, anthracnose, and red rust. Therefore, accurate and timely disease diagnosis is crucial in controlling and managing these diseases. In this study, an Expert System for Disease Diagnosis in Crystal Guava Plants Using the Dempster Shafer Method-Based Web was developed. The Dempster Shafer method calculates the likelihood of a disease attacking crystal guava plants based on the density probability values of each symptom. The results produced by this expert system include the type of disease, solution, and the percentage probability value of the disease, with an average accuracy of 83.33%. Hence, it can be concluded that the system can diagnose diseases in crystal guava plants effectively.

SISTEM PAKAR DIAGNOSIS PENYAKIT MELALUI GENERAL CHECK UP BERBASIS MOBILE APPLICATION ANDROID

Expert System is a system created by imitating the performance or thought patterns of humans, which is input into an application system with the aim of helping to facilitate human work as done by experts. Android-based Expert System can help users analyze what diseases are present in the body. Expert System for Disease Diagnosis Through General Check Up Based on Android Mobile Application at Bojong Medika Clinic is a mobile-based application system that can be used as a platform for users to record, store medical records, automatically process data from general check-ups, and remind them to do further general check-ups in an effort to make everyone using this application more concerned about their health and avoid various diseases. This application is created using Kodular and supported by Air Table as its database, with additional tools using Firebase. The system development method used is the Waterfall Model, starting from the analysis stage, then followed by design, implementation, and finally testing of the application system.

Application of Forward Chaining and Rule-Based Reasoning Methods to Design an Expert System for Pregnant Women Disease Diagnosis in a Private Hospital

This research aims to discover how to create and implement an expert system for prenatal disease diagnosis. To prepare this study, the author employed several data collection techniques, including observation, interviews, and a literature review. The system to be designed was developed using an object-oriented paradigm and a Rapid Application Development (RAD) technique. Multiple development stages are involved in creating RAD, including requirement planning, workshop design, and implementation. Based on the study findings, it was discovered that the PHP programming language, MySQL database management system, Apache web server, UML tool, and RAD system development methodology were used to create an expert system for identifying illnesses in expectant mothers. The forward chaining method is used for this expert system, where the first stages are to create an inference tree and set rules. This system likewise computes the consistency test using the Bayes theorem method. With an 80% accuracy rate, SISPADIBU can be used as a diagnostic tool for disorders related to pregnancy. SISPADIBU can use the user-entered symptoms to present the diagnosis results. According to the survey results, 80% of users said that SISPADIBU was highly user-friendly, that the layout display was simple to use, and that it was constructive in educating users about pregnancy disorders.

Gynaecological Disease Diagnosis Expert System (GDDES) Based on Machine Learning Algorithm and Natural Language Processing

Gynaecological Disease Diagnosis Expert System (GDDES) Based on Machine Learning Algorithm and Natural Language Processing

Development of Cattle Disease Diagnosis Expert System (CaDDES): A web application for the diagnosis of cattle diseases

In the present study, an attempt was made to develop an expert system for the diagnosis of cattle diseases under field conditions by Veterinarians. A questionnaire tool was developed and used for collecting the scores for symptoms or signs of 13 cattle diseases for diagnosis. The tool was tested for face validity by pilot survey with experts and content validity by calculating Aiken’s value index. The scores for the signs or symptoms were collected from 178 field veterinarians from different states in India. The scores obtained were tested for reliability and consistency by using the split-half test [88.7%] and Cronbach’s alpha coefficient [92.7%], respectively. The weighted matrix was calculated for the 51 signs or symptoms obtained for 13 cattle diseases. The CaDDES, a web application wasdeveloped using PHP, HTML, JavaScript, and MySQL computer programming languages. This web application contains Home, Introduction, About CaDDES, Disease Diagnosis, Development Team, and Contact pages. The Veterinarians need to enter the basic details of cattle and select the signs or symptoms observed in the particular cattle under field conditions. The results page will display the three probable diseases diagnosed for the selected signs or symptoms. Further, the necessary samples to be collected for laboratory confirmation and preventive measures to be undertaken were also provided. This web application will be useful for field level diagnosis of cattle diseases by Veterinarians and later by farmers. It may also be useful as a surveillance application, which will improve cattle disease reporting in the future.

Expert System for Diagnosis of Lung Disease from X-Ray Using CNN and SVM

The lung disease diagnosis expert system utilizes human knowledge to diagnose various conditions affecting the lung. Diseases caused by fungal or bacterial infection in the organ can cause inflammation as well as death when it is not detected on time. A standard method to diagnose these conditions is the use of a chest X-ray (CXR), which requires careful examination of the image by an expert. In this study, several CNN and SVM architectural models were proposed to classify CXR images to diagnose whether a person has COVID-19, Viral Pneumonia, Bacterial Pneumonia, Tuberculosis (TB), and Normal. The experiment showed that InceptionV3 had the best results compared to other CNN architectures and SVM. Classification accuracy, precision, recall, and f1-score of CXR images for COVID-19, Viral Pneumonia, Bacterial Pneumonia, TB, and Normal were 0.86, 0.91, 0.91, and 0.91, respectively. This study was based on a deep learning system with different CNN and SVM architectures that can work well on the CXR images dataset for diagnosing lung disease.

Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

DESIGN OF AN ANDROID-BASED LUNG DISEASE SCREENING EXPERT SYSTEM AND PATIENT EARLY WARNING USING THE FORWARD CHAINING METHOD AT WALUYO JATI, KRAKSAAN HOSPITAL

This aims of study were design and create an android-based lung disease screening expert system and patient early warning using the forward chaining method at Waluyo Jati Kraksaan Hospital. This study used a mixed methods approach, namely descriptive qualitative and quantitative descriptive. In this descriptive qualitative research design used case study and the quantitative descriptive research design uses a survey. This research used the forward chaining method and model development using the Waterfall model. Based on the accuracy test table, it can be concluded that the Android-based lung disease diagnosis expert system uses the Forward chaining method. The ISO/IEC 9126 standard test uses two aspects, namely the usability aspect of 83.45% and the Functionality aspect of 85.1%. The certainly factor for the confidence value can be identified quite well. This is evidenced by the results of testing the system accuracy of 96.43%. An Android-based lung diagnostic expert system was built to provide identification conclusions in accordance with the thoughts of an expert, namely a lung specialist.

A Proposed Model Expert System for Disease Diagnosis in Children to Make Decisions in First Aid

Children have a weaker immune system than adults. They are susceptible to disease. Therefore, this study proposes an expert system model of disease diagnosis in children. We develop expert strategies to meet the needs of alternative diagnostic tools in making decisions and first aid for children suffering from illness. The development of an expert system model for diagnosing children's diseases using forwarding chaining based on If-Then as an inference engine. We chose the forward chaining method because it has a framework for thinking like a doctor's when diagnosing and concluding the disease. We made testing to model by doctors with 35 patients. The test results show that the expert systems model of disease diagnosis in children in this research has to be used as an alternative or comparison diagnostic tool with an accuracy rate of 79%.

Sistem Pakar untuk Mendeteksi Gejala Awal Penyakit Apendisitis dengan Metode Case Based Reasoning (CBR) Berbasis Mobile Android

Appendicitis is caused by inflammation of the intestines (appenditis). The patient will feel pain in the lower right abdomen. This study explores this with several references, by designing an expert system an application is produced that is used to detect appendicitis. This process can help detect early symptoms starting from the user answering questions in the form of symptoms suffered by the user. The research applies the Case Base Reasoning method in the expert system by detecting the early symptoms of appendicitis using this android-based device. The research aims to add experience to users in finding out the disease they feel by entering the initial symptoms and providing solutions or it can be without consulting the nearest hospital or clinic. The results of the research. This android-based appendicitis expert system aims to help diagnose Appendicitis Inflammatory Disease for children under five and adults based on Android Mobile, and the Appendicitis Disease Diagnosis Expert System that was built to provide information easily starting from understanding, dangers, causal factors, symptoms of the disease and solution only by consulting the system and objectives can help provide a good solution.

Sistem Pakar Diagnosa Penyakit Lambung Berbasis Website Menggunakan Metode Forward Chaining & Certainty Factor

Gastric disease is a disease that cannot be underestimated, because if left unchecked it can result in other diseases appearing and can also cause death if not treated immediately. Diseases of the stomach can be caused by an inappropriate diet, a burden on the mind and also infections caused by bacteria. The little public knowledge of the early symptoms of gastric disease is the author's background to create a system that helps the public in obtaining information, consultation or initial treatment for gastric diseases without having to meet in person. The ability of an expert who can diagnose stomach diseases can be implemented into an application system. The methodology used in making the Gastric Disease Diagnosis Expert System Website is waterfall, namely analysis, design, implementation and testing. The testing phase focuses on the functional specifications of the program. In this Expert System, it uses the Forward Chaining method as reasoning and the Certainty Factor method to calculate the results of the yield trust. Hasil testing shows that this website can perform its function to diagnose gastric diseases. The test results are obtained from the inputs made by visitors by selecting the options that have been given and then will get the test results.

Sistem Pakar Diagnosis Penyakit Ayam Broiler Berbasis Web

Mr. Jumaldi Jani, SP., MM Broiler Chicken Farm is a chicken farm that was founded in 2013. In carrying out its business processes, handling of raising and developing poultry is only handled by one person who is taken as the farm supervisor. This is very ineffective in handling diseases that attack chickens because the knowledge of the maintenance and handling of chickens that have disease is only owned by one person. The purpose of this study is to create a Web-Based Chicken Disease Diagnosis Expert System, which can be accessed easily, quickly, economically, can be done anytime or anywhere without knowing the distance and time. The method used in this research is web engineering which has stages: customer communication, planning, modeling, construction and deployment. The establishment of a Web-Based Broiler Chicken Disease Diagnosis Expert System is expected to facilitate the public about conducting consultations on Broiler chicken disease easily, quickly, and economically, it can be done anytime and anywhere without looking for distance and time.

Analyzing Healthcare Data Using Water Wave Optimization-Based Clustering Technique

The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.

Knowledge Base Design for Diagnosis Pregnancy Problems

In Indonesia, the death rate of pregnant women was still very high. Lack of knowledge about the perceived symptoms during pregnancy make pregnant women regardless of the specific symptoms that can be harmful and disease records indicate the causes of indirect maternal death in pregnant. Moreover, the risk of pregnant mother mortality is also higher due to the delay in taking the decision factors for referenced. Based on the fact, the proposed solution in the form of expert system diagnosis of diseases of pregnant women using the method of Backward Chaining that can help recognize diseases during pregnancy to take place based on the perceived symptoms of pregnant women as well as references that should be targeted by the patient. The methods of the Backward Chaining have a performance system that is capable of running the functional needs and high accuracy percentage results. With the existence of a system of experts on pregnancy problems that use the Backward Chaining method can describe the level of confidence of an expert on the problem at hand. Based on the test results, obtained results 100% functionality of disease diagnosis expert system of pregnant women worked in accordance with a list of system requirements and the system.

Web-Based Rice Disease Diagnosis Expert System Using Fuzzy Tsukamoto Method and K-Nearest Neighbor Algorithm

Technology today is growing rapidly from year to year, not least started to spread to the agricultural sector. With the information technology making society more easily in search of information via the internet from your smart device. The goal of this study was made to facilitate the community, especially farmers in helping to diagnose diseases and pests in rice plants. Rice plants can be attacked by a wide variety of diseases and pests with a wide variety of symptoms experienced in rice plants. To know the kind of disease on rice plants in the era of technology, it takes an expert system that can help detect the disease in rice plants. In this study, Expert System-Based Website using Tsukamoto Fuzzy method and the Algorithm of K-Nearest Neighbor whose purpose is to help people, especially farmers in diagnosing diseases and pests in rice plants by looking at the symptoms of the attack on the rice plant. Data was obtained from the Research and the Ministry of Agriculture then taken some sample data for testing done. The results of the testing data of this expert system is the result of late diagnosis in diseases of the rice with the symptoms that already exist based on the data that have been obtained with an accuracy rate of 92,88%.

Cattle Disease Diagnosis Expert System in Bandes Village, Ainaro District

Most of the people in the Bandes village of Ainaro district work as breeders. Cows that are raised are beef cattle. One of the obstacles that exist in Bandes Village is the existence of a few veterinarians in Bandes Village. This research discusses an expert system for diagnosing diseases in cattle, which functions to help determine the disease that is being suffered by cattle, this system will display the results of the diagnosis in the form of the name of the disease, a description of the disease solution and its prevention. In this study there were 4 diseases studied and 7 symptoms. From the 4 disease data, isolation of the problem area, target decision and dependency diagram is made. After that the formation of IF – THEN rules, after the rules are made, the backward chaining process is made. So as to produce a solution to overcome disease in cattle. In program testing is done by comparing the results of the system with experts. In the trials that have been carried out, the results obtained are appropriate or the accuracy of the data and facts obtained from experts on cattle. So based on the results of tests that have been carried out on the system as much as 4 data, the accuracy value obtained is 100% accurate which shows that the expert system is functioning properly in accordance with the expert's diagnosis

Web-Based Expert System for Diagnosing Gastric Disease Using Bayes Theorem Method

Health is a very expensive thing, so it takes an awareness to protect it. The human body has many limbs, one of which is the stomach. Gastric disease is a disease that cannot be accommodated, because if left untreated it can cause other diseases to arise and can also cause death if it is not immediately supported. Gastric disease can be caused by an inappropriate diet, a burden on the mind and also an infection caused by bacteria. The author took the initiative to create a web-based gastric disease diagnosis expert system using the Bayes theorem method which is useful for gastric disease sufferers in diagnosing their own gastric disease. The method used by researchers is the Bayes theorem method. The data collection technique used is quantitative data techniques. The results that have been achieved from this research are that it can prevent and minimize the risk of gastric disease in sufferers or the community.