Diagnosis Expert System Research Articles

Expert System For Diagnosis of Mental Health Disorders in Students Using Case-Based Reasoning Method With a Web-Based Positive Psychology Approach

Mental health issues among students have become a significant concern affecting their quality of life and academic performance. An effective expert system is needed to diagnose and provide appropriate interventions. This research develops a web-based expert system that utilizes the Case-Based Reasoning (CBR) method combined with a positive psychology approach to diagnose mental health disorders in students. The CBR method identifies similarities between new and previous cases, while the positive psychology approach focuses on individual strengths and potential for growth. The system integrates a database of student mental health cases and CBR algorithms to produce relevant diagnoses. This study investigates four types of mental health disorders: panic, anxiety, stress, and depression. The method used for data analysis is Case-Based Reasoning. The diagnosis results are based on calculations from symptom choices within the system, where each symptom has a weight. The highest similarity calculation obtained from past cases is used as a solution to address the problem. System testing, based on expert knowledge with 15 test data samples categorized by mental health disorders and 38 symptoms, achieved an accuracy rate of 85%.

A clinical expert system for Noma disease diagnosis

A clinical expert system for Noma disease diagnosis

Android-Based Expert Diagnosis System of Children’s Ear, Nose, and Throat Disorders Using Forward Chaining Method

Ear, nose, and throat disorders are a significant health problem, especially in children. This article discusses the development of an expert system as a diagnosis tool for ENT disorders in children by applying the forward chaining method. The system development was carried out using the waterfall method and built based on android using Kodular. The system can diagnose 36 disorders with 42 symptoms obtained by literature study technique from the book First Aid in Your Home written by Dr Tony Smith and Dr Sue Davidson. The result of this system is able to help the process of diagnosing ENT disorders in children accurately based on knowledge from experts through android applications.

A privacy-preserving expert system for collaborative medical diagnosis across multiple institutions using federated learning

Expert system recommendation assists the healthcare system to develop in real-time monitoring and diagnosis of patient conditions over several healthcare institutions. Privacy concerns, however, present significant problems since patient data leaks can lead to big effects including financial losses for hospitals and invasions of personal privacy for people. To address these issues, the research introduces a privacy-preserving collaborative medical diagnosis (CMD) method on a federated learning (FL). FL maintains patient privacy and data localization by spreading only model parameters, therefore enabling training models on remote datasets. The combination of Partially Homomorphic Cryptosystem (PHC) and Residual Learning based Deep Belief Network (RDBN) ensures an accurate and safe classification of patient physiological data. Experimental results show that the proposed method is successful in maintaining the diagnostic accuracy over numerous healthcare institutions and protecting privacy. The results show that the RDBN and PHC computations requires around 1000 ms and 150 ms, respectively for classification and privacy; the data transmission from the user to server and from server to user is 5 MB and 4 MB, respectively. Finally with a 30% reduction in overhead, the proposed approach offers an average increase in classification accuracy of 10% over multiple datasets.

Development of an Expert System for Diagnosing Musculoskeletal Disease

Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.

Pengembangan Aplikasi Sistem Pakar Preferensi ‘Bahasa Cinta’ Manusia Berbasis Web

The purpose of this research is to produce a web-based expert system application to identify a person's love language using the nave Bayes classifier method. This expert system application to identify a person's love language is intended for people who have not realized how important it is to know which love language is relevant to them. This expert system application to identify a person's love language provides guidance in the form of questions about the type of love language. The answers to the questions asked will lead to the final decision on the results of the expert system diagnosis. Sources of expert system application knowledge base obtained from books and the internet. In making the application using the extreme programming software development method. Extreme programming consists of four stages, namely: planning, design, coding, and testing. The result of this research is the application of an expert system of love language identification that works on a web base.

A novel CLIPS-based medical expert system for migraine diagnosis and treatment recommendation

Migraines are classified as a neurological disorder defined by recurrent headaches with pain that ranges from mild to severe. Currently, this disorder lacks a permanent cure and definitive diagnostic test. Diagnosis instead requires an assessment of physical and psychological symptoms which differ among patients. To help in the diagnosis process, medical expert systems have been developed and validated since 1960. In this paper, we propose the Migraine Diagnosis and Treatment Expert System (MDATES), a medical expert system for migraine diagnosis and treatment recommendation. The system was designed and implemented using the C Language Integrated Production System (CLIPS) shell. MDATES is able to recognize seven symptoms, two classes of migraines (chronic and episodic), and four subtypes of migraine-classification knowledge (hormonal, aura, hemiplegic, and cluster). A dataset of 300 anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by MDATES were compared against the known diagnoses, and a high level of accuracy was observed, with 67% of the 100 training cases were correctly diagnosed, and 100% of the 200 testing cases were correctly diagnosed. These results highlight the effectiveness and reliability of MDATES and provide valuable assistance to medical professionals in diagnosing migraines. Moreover, we present a literature review that places our proposed system within the broader context of rule-based expert systems for migraine diagnosis and treatment recommendation. This review explores the effectiveness, limitations, and challenges of these systems, and accurately places our system within the current landscape.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

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

Expert system for diagnosing learning disorders in children

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings.

SYNDIAG: an expert system for disease syndrome diagnosis of traditional Vietnamese medicine

SYNDIAG: an expert system for disease syndrome diagnosis of traditional Vietnamese medicine

Research on the optimization of belief rule bases using the Naive Bayes theory

The belief rule base is crucial in expert systems for intelligent diagnosis of equipment. However, in the belief rule base for fault diagnosis, multiple antecedent attributes are often initially determined by domain experts. Multiple fault symptoms related to multiple antecedent attributes are different when an actual fault occurs. This leads to multiple antecedent attributes matching with multiple fault symptoms non-simultaneously, thereby resulting in a fault diagnosis lacking timeliness and accuracy. To address this issue, this paper proposes a method for belief rule-based optimization based on Naive Bayes theory. First, a fault sample is taken in a long enough window and divided into several interval samples, making the analysis samples approximate the overall samples. Second, using Gaussian mixture clustering and Naive Bayes optimization, iteration is performed over the threshold and limit values of fault symptoms in the belief rule base based on the requirements of the timeliness and accuracy of fault diagnosis results. Finally, the belief rule base is optimized. Using fault samples from high-pressure heaters and condensers, the validation results show that there is a there is a significant improvement in the timeliness and accuracy of fault diagnosis with the optimal belief rule base.

Application of Dempster-Shafer Theory Method in Expert System for Diagnosis of Psychological Disorders in Children

Application of Dempster-Shafer Theory Method in Expert System for Diagnosis of Psychological Disorders in Children

Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.

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

PERANCANGAN SISTEM PAKAR DALAM MENDIAGNOSA KERUSAKAN CCTV BERBASIS WEB MENGGUNAKAN METODE FORWARD CHAINING (STUDI KASUS: PT.MNC PICTURES)

The use of Closed Circuit Television (CCTV) has almost become a common necessity in the current era, especially for security, where it is to record an incident 24 hours a day which can be immediately broadcast or stored as evidence if one day it is needed. Closed Circuit Television or better known as CCTV is equipment that functions to record events in real time in a room. The research objective is to build a web-based expert system that is able to diagnose damage to CCTV and implement the forward chaining method. Data collection methods are observation, interviews and literature study. Meanwhile, the research method used is forward chaining. The result is that an expert system can be built on a web basis and is able to diagnose damage to CCTV and also the forward chaining method can be implemented to overcome the uncertainty of the expert system diagnosis results.