Expert System For Medical Diagnosis Research Articles

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.

Building an enhanced case-based reasoning and rule-based systems for medical diagnosis

Expert systems are computer programs that use knowledge and reasoning to solve problems typically solved by human experts. Expert systems have been used in medicine to diagnose diseases, recommend treatments, and plan surgeries. Interpretability of the results in medical applications is crucial since the decision that will be taken based on the system’s output has a direct effect on people’s health and lives which makes expert systems ideal choices when dealing with these applications in contrast to other machine learning approaches. An expert system has the ability to explain its own line of reasoning providing a robust way of diagnosis. This paper presents two types of expert systems for medical diagnosis. The first system is a case-based reasoning system using a database of previously diagnosed cases to diagnose a new case. The second system is a rule-based expert system that uses a set of if–then rules extracted from a decision tree classifier to make diagnoses. In this paper, machine learning-based similarity functions are proposed and compared with other traditional similarity functions. The results of this study suggest that expert systems can be a valuable tool for medical diagnosis. The two systems presented in this paper achieved competitive results, and they provide diagnoses similar to those made by human experts.

Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images

Abnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.

A Novel Expert System for the Diagnosis and Treatment of Heart Disease

The diagnosis of diseases in their early stages can assist us in preventing life-threatening infections and caring for them better than in the last phase because prevention is better than cure. The death rate can be very high due to the unapproachability of diagnosed patients at an early point. Expert systems help us to defeat the problem mentioned above and enable us to automatically diagnose diseases in their early phases. Expert systems use a fuzzy, rule-based inference engine to provide forward-chain methods for diagnosing the patient. In this research, data have been gathered from different sources, such as a hospital, by performing the test on the patients’ age, gender, blood sugar, heart rate, and ECG to calculate the values. The proposed expert system for medical diagnosis can be used to find minimum disease levels and demonstrate the predominant method for curing different medical diseases, such as heart diseases. In the next step, the diagnostic test at the hospital with the novel expert system, the crisp, fuzzy value is generated for input into the expert system. After taking the crisp input, the expert system starts working on fuzzification and compares it with the knowledge base processed by the inference engine. After the fuzzification, the next step starts with the expert system in the defuzzification process converting the fuzzy sets’ value into a crisp value that is efficient for human readability. Later, the expert physician system’s diagnosis calculates the value by using fuzzy sets, and gives an output to determine the patient’s heart disease. In one case, the diagnosis step was accomplished, and the expert system provided the yield with the heart disease risk level as “low”, “high”, or “risky”. After the expert system’s responsibilities have been completed, the physician decides on the treatment and recommends a proper dose of medicine according to the level the expert system provided after the diagnosis step. The findings indicate that this research achieves better performance in finding appropriate heart disease risk levels, while also fulfilling heart disease patient treatment due to the physicians shortfalls.

FPGA Implementation of Expert System for Medical Diagnosis of Disc Hernia Diagnosis Based on Bayes Theorem

The aim of this research is to create a medical expert system based on Bayes theorem to diagnose level of disc hernia based on real foot force measurement signals obtained using sensors and implement the whole system on field programable gate array (FPGA). We have created a database of attributes based on recorded foot force values of 33 patients pre-diagnosed with herniated disc on levels L4/L5 or L5/S1 on the left or right side. The results obtained by software (Matlab) and hardware (FPGA simulation) are matching well, achieving high accuracy, which shows that VHDL implementation of Naïve Bayes theorem for disc hernia diagnostics is adequate. The output on FPGA is easy to understand for any user, as it is implemented as four-bit output where the position of bit value 1 indicates the level of disc herniation. The system is able to distinguish between the healthy subjects and subjects with disc herniation and is able to detect if improvement in stability is present after surgery or physical therapy. Our proposed measurement platform can be coupled with FPGA to create a portable and not expensive tool for real time signal acquisition, processing and decision support system in disc hernia diagnosis and post-surgical recovery.

Design and implementation of a medical expert system for diagnosis and prescription

This project work focuses on computerization of medical diagnosis and prescription system to increase medical services and enhancement of logistic efficiency. The aim is to produce approach which through computer programs and files carefully created, written, designed and integrated from an application package that accepts both physical states of patients. Disease symptoms (input data) and as such one which is able to store, access, manipulates as much information as possible. The ease and speed of processing, storage and versatility of the computer make it possible for easy diagnosis and prescription interestingly; the end results is the ability analysis of the disease by producing the input data and provide summaries of the total treatment bill, lab result and other disease investigation and confirmation for a particular patient.

Deep learning-based medical expert system for diabetes diagnosis on IoT healthcare data

Deep learning-based medical expert system for diabetes diagnosis on IoT healthcare data

Deep learning-based medical expert system for diabetes diagnosis on IoT healthcare data

Machine learning in internet of things (IoT) plays a vital role in diagnosing diseases and predicting the risk level of health by analysing patient health records. Diabetes has been commonly seen i...

Fuzzy Logic Controller of New Strategy of Biomedical Measurements

<p class="Abstract">Monitoring patient’s condition represents one of the crucial tasks in any biomedical center to ensure the stability of the patient state. Healthcare sector quality demands are exponentially rising. These demands are concerned with designing expert systems for medical diagnosis. Also, the use of fuzzy sets theory in the medical field is to solve some medical problems. </p><p class="Abstract">This paper proposes a simple strategy of bio measurements and its behavior through fuzzy logic model. The proposed design is constructed using three different types of data transfer based sensors and microcontroller.</p><p class="Abstract">The fuzzy logic model is designed and practically tested. The generated results are compared to those of the (Omron RS3 - HEM-6130-E Omron mc-246-e) device from OMRON company. Four hundred people aged between 3 and 72 years old were examined by the proposed system. All results are compared using fuzzy logic model in MATLB. Using statistical analysis, the average data measurements for each sensor in the designed device were recorded; the output results were found to be of close enough (less than 1% error) to the reference readings.</p><p class="Abstract">The system can be used at home due to its low cost, easy to use, and high accuracy.</p>

Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network.

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error - 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom. Graphical abstract.

A Novel Fuzzy Logic-Based Medical Expert System for Diagnosis of Chronic Kidney Disease

Chronic kidney disease is a life-threatening complication. Primary diagnosis and active control avoid its progression. To increase the life span of a patient, it is necessary to detect such diseases in early stages. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed. The proposed fuzzy rule-based expert system is developed with the help of clinical practice guidelines, database, and the knowledge of a team of specialists. It makes use of input variables like nephron functionality, blood sugar, diastolic blood pressure, systolic blood pressure, age, body mass index (BMI), and smoke. The normality tests are applied on different input parameters. The input variables, i.e., nephron functionality, blood sugar, and BMI have more impact on the chronic kidney disease as shown by the response of surface analysis. The output of the system shows the current stage of patient’s kidney disease. Totally 80 tests were performed on the FES developed in this research work, and the generated output was compared with expected output. It is observed that this system succeeds in 93.75% of the tests. This system supports the doctors in assessment of chronic kidney disease among patients. The detection of chronic kidney disease is a serious clinical problem that comprises imprecision, and the use of fuzzy inference system is suggested to overcome this issue. The proposed FES is implemented in the MATLAB.

A privacy-preserving group decision making expert system for medical diagnosis based on dynamic knowledge base

Most of the present researches on wise medical incorporate a static supporting data, while medical knowledge and rules are featured with a dynamic change, which affect calculation efficiency and accuracy of the result. This paper combined data mining technology with expert system framework, established a medical expert system with a dynamic knowledge base. In the innovative dynamic system, samples were together with their classification results would be added to the knowledge base. In addition, privacy protection was considered in the expert system by means of data removal and conversion of attribute value. At last, for a comprehensive and precise evaluation of both numeric and character attribute data, we proposed a new similarity measurement algorithm termed as E–JD, which combinate Euclidean Distance algorithm with Jaccard Distance algorithm. For more improvement of the data classifasion accuracy, based on KNN and the dynamic knowledge base, the innovative algorithm of D-SNN was proposed, with a modification in the selection and decision of the nearest neighbor objects. The experimental comparison between 1NN, KNN, KNN-means and D-SNN algorithm, shows that the classification performance of the proposed algorithm, D-SNN, exhibits an impressive improvement.

Data Fusion and Machine Learning in Medical Diagnosis: A Bird Eye View

With the invent of computer-mediated technologies, urge of medical diagnosis, surveillance system and the rapid development in satellite and sensor networks, demands an efficient data fusion techniques, methodologies and machine learning algorithms. Expert system and Data fusion has materialized as a promising research area for medical diagnosis in the upcoming years. In Data fusion, information may be in various nature: it ranges from measurements to verbal reports. Data fusion is a framework for analysis of data sets such that different datasets can interact and inform each other. Machine learning together with data fusion provides results with high accuracy and prediction. This paper presents a comparative analysis of existing expert systems for medical diagnosis which uses data fusion and machine learning algorithms to diagnose various diseases.

A Medical Expert System for Tropical Disease Diagnosis

A Medical Expert System for Tropical Disease Diagnosis

Presenting a Medical Expert System for Diagnosis and Treatment of Nephrolithiasis

Expert systems aim to employ expert skills for non-expert person. These designs simulate intellectual and practical performance of human making the performance of expert systems close to that of an expert human. Various expert systems have been proposed in medicine, thus this area is attracting attention. Most problems in modern medicine are very complicated and there is no logic reason for accurate decision making. To this end, doctors decide arbitrarily and variably. On the other hand, large volume of medical information makes decision making more difficult while modern technologies add to volume of information and make problems more difficult. Considering these problems, there is a great challenge in medical diagnosis which requires decision-making support systems. In this paper, an expert system is presented for diagnosis and treatment of nephrolithiasis in which knowledge required for diagnosis and treatment is stored as rules in the system knowledge base. If experts are absent, diagnosis and treatment can be done reliably. Expert systems can be used as decision support by the users. However, currently, they cannot replace experts.

Construction and Implementation of an Expert System for Medical Diagnosis Based on Blood Test

The recent developments in the field of artificial intelligence to the emergence of expert systems, and computational tools designed to capture and access to knowledge from experts in the medical field. We have built an expert system for the purpose of giving the diagnosis of some diseases by linking this system devise testing and analysis of blood type (CELL-DYN Ruby) and which has a private entrance to the computer. The system is divided into several modules, History Entering Module (HEM) systematic Review Module (SRM), a Laboratory Test List Module, Diagnosis Process Module (DPM), Data Base (DB), and finally the graphical interface to the user.
 All these modules work together to give a high degree of accuracy in the diagnosis of disease through blood samples and we will describe the work of each unit in detail.

A Contemporary Research on Perceptual Expert System for Medical Diagnosis through Neural Dust Sensor

Abstract In the past few years many researches have been dedicated towards Neural dust sensor such as Deep Brain Simulation (DBS), Brain Machine interface (BMI), Ultrasonic backscattering system and so on. Mainly the Brain-Machine Interface is considered as the neural interface system that can be usable for a lifetime and to increase the life span of the sensor with the implementation of wireless electromagnetic neural interface. By implementing all this to detect the blood clot in human brain and to reduce the blood clot using ultrasonic waves and further other implementation in medical field. And, Neural Dust sensor is considered as the central nervous system of a particular system. This study aims to provide a review on usage of neural dust sensor in medical diagnosis.

Medical Diagnosis Expert System for Malaria and Related Diseases for Developing Countries

There is a strong need for cost-effective technologies to manage disease processes and thus reduce morbidity and mortality in the developing countries. Yet bringing intelligent healthcare informatics to bear on the dual problems of reducing healthcare costs and improving quality and outcomes is a challenge even in countries with a reasonably developed technology infrastructure. This paper focused at how appropriate an ap-plication of Medical Diagnosis Expert System (MDES) is to manage diseases in developing countries. MDES is usually designed to enable clinicians to identify diseases and describe methods of treatment to be carried out taking into account the user capability. The MDES described here is implemented using the C Language Integrated Production System (CLIPS). The CLIPS is an expert system, which has a shell composed of four modules: the user interface, the explanation system, the inference engine and the knowledge base editor. In the system, a number of patient cases will be selected as prototypes and stored in a separate database. The knowledge is acquired from literature review, human experts and the internet of the specific domain and is used as a base for analysis, diagnosis and recommendations.

A medical decision support system for disease diagnosis under uncertainty

We present a decision support system (DSS) modeled for medical diagnosis.Fuzzy Expert System is used to implement the proposed system.Two approaches for uncertain input values are proposed.A case study on kidney stone and kidney infection diseases was conducted. This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.

Fuzzy Expert Systems (FES) for Medical Diagnosis

Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems based on the expert’s knowledge and observations. This paper reviews the trend in development of FES and application potential over past two and half decades in the medical field, based on the references of 173 articles from 124 journals, several proceedings and web media. In order to investigate the significance of FES for medical diagnosis, the articles are classified into five distinct categories: Reviews and Surveys on Fuzzy Expert Systems in Medical Diagnosis, Applications of Fuzzy Expert Systems in Medical Diagnosis, Methodologies and Modelling of Fuzzy Expert Systems, Neuro-Fuzzy Approaches, Fuzzy Expert System Shells and Frameworks. The development of disease specific applications using FES is observed to be the area of most significant interest of the researchers. The earlier contributions are reviewed, classified, analyzed and suggested the future scope.