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The aim of this study is to design a Fuzzy Expert System for heart disease diagnosis. The designed system based on the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data base. The system has 13 input fields and one output field. Input fields are chest pain type, blood pressure, cholesterol, resting blood sugar, maximum heart rate,...
Citations
... Another group of researchers in [32][33][34][35][36][37][38][39] focuses on the diagnosis of heart diseases. With a different number of input and output attributes, authors in [40][41][42][43][44][45][46][47][48] present different levels of accuracy percentages. The achieved accuracy is 63.24% [47] to 94.05% [44] based on various input and output features implemented in Mamdani inference systems. ...
... The achieved accuracy is 63.24% [47] to 94.05% [44] based on various input and output features implemented in Mamdani inference systems. Literature [40] displays 94% accuracy using 44 rules in the fuzzy expert system with eleven inputs and one output. The accuracy is increased a bit more (94.05%) in [44] as the work introduced decision tree algorithm with the fuzzy expert system. ...
... Similarly, other rows of this table are formed. The approach described in the literature is used to construct 4320 inference rules in our fuzzy expert system [40]. Following that, we modified the Cleveland database's detection rules for heart disease in the UCI repository. ...
Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.
... Deep learning models are based on numerous algorithms, and so these algorithms have become significant for properly predicting the presence or absence of cardiac ailments. Different researchers all over the world have worked on heart disease prediction using various techniques of machine learning, deep learning, and fuzzy logic [9,10], but still there are some shortcomings, which are given below: ...
This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on "Early Heart Disease Prediction using Boosting Techniques", this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost's superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction.
... In addition to using a computational method with a C4.5 decision tree approach to find cardiac disorders, many approaches are used to find cardiac disorders, such as the use of an expert system with a certainty factor approach that is built based on a person's expertise. which was adopted into an application Int J Artif Intell ISSN: 2252-8938 Classification of cardiac disorders based on electrocardiogram data using a decision tree … (Sumiati) 1129 [1]- [3] diagnosis of heart disease with decision tree algorithm for prediction and classification [4]- [16], development of an android-based diagnosis system for heart disorders [17], Fuzzy Expert System for Diagnosing Heart Disease [18], authentication method in utilizing electrocardiogram (ECG) wave features [19], application of IoT for disease diagnosis [20]- [23], single lead ECG classification with approach deep learning [24]- [26], signal analysis system for ECG authentication system [27], improve the diagnosis of heart disease with particle swarm optimization (PSO) Algorithm evolutionary approach and neural network [28], improving the heart disease diagnosis by evolutionary algorithm of PSO and feed forward neural network [29]. ECG classification using the k-nearest neigbor (KNN) approach [30]. ...
p>The limitations of medical personnel, especially heart disease, cause difficulties in diagnosing heart disorders, so diagnosing heart disorders is not easy, it takes the ability and experience of a cardiologist who has the expertise and experience to be able to accurately diagnose heart disorders. Several studies in the field of computing have been carried out in diagnosing cardiac abnormalities in patients. This study was conducted to accurately test the results of the classification of heart disorders using electrocardiogram medical record data with a C.45 decision tree approach. The results showed that the classification of heart defects obtained a mean squared error (MSE) value of 0.24, a root mean squared error (RMSE) value of 0.49, and an accuracy value of 75.33% with the C4.5 algorithm.</p
... However, predictions and environmental decision-making are often limited due to the uncertainty, vagueness, or ambiguity of observational data [e.g. [3][4][5][6][7]. The methods of statistical mechanics have embraced two-valued logic-based probability theory, wherein a random variable is used as the basis of probability computations. ...
... The output revealed 91.58% accuracy of the model. Adeli and Neshat [7] designed a fuzzy expert system using Mamdani inference with 13 inputs and one output to determine coronary heart disease risk. Anooj [8] developed clinical decision support systems (DSSs) to determine the risk level of heart disease using weighted FRBS. ...
... Atherosclerosis is a prominent cause of death globally. Professionals, on the other hand, typically have trouble detecting heart illness owing to a high degree of ambiguity and a risk factor [1]. When a heart attack happens, speed is of the essence in saving the patient's life and avoiding heart failure. ...
Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. e system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.
... Adeli and Neshat developed a heart disease risk model using a fuzzy expert system [17]. e membership function of all the 11 input variables and 1 output variable utilizes an inference mechanism. ...
Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.
... In order to address these concerns, this study presents security techniques for accessing personal health records (PHR) in the cloud. PHR is first established as a basic storage service, but it is eventually changed to a cloud-based healthcare system that patients may administer themselves [20]. To a large part, the present PHR system is focused on facilitating quick access to health information, illness management, and information exchange. ...
... For this input field, the value of low-density lipoprotein (LDL) cholesterol is used. e cholesterol field has four fuzzy sets (low, medium, high, and very high) [9,20]. e membership graph is shown in Figure 4. Membership expressions for cholesterol are defined in equations (1) and (2). ...
It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient’s criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.
... In the study of [33], a fuzzy rule-based system was designed to serve as a decision support system for diagnosis Coronary heart disease. In the work [6] a Fuzzy Expert System for heart disease diagnosis using V.A. Medical Center, Long Beach and Cleveland Clinic Foundation database was designed and system is being designed with in Matlab software and it is viewed as an alternative for existing methods to distinguish of heart disease presence. ...
Coronary artery disease (CAD) is one of the most dangerous diseases which lead to sudden cardiac death. According to Word Health Organization, CAD is the number one killer in the developed world, with over 7.4 million deaths attributed it. Before, CAD is not common disease in Nigeria, however, is at this moment gaining much popularity in the country following the rising number of health issues related to CAD diseases, including higher death rate, which is mostly due to lack of proper awareness among the common people. The diagnosis of CAD is very expensive and time consuming which made computer scientists to use artificial techniques such as expert system to diagnose CAD's patients. Therefore, in this work, fuzzy based expert system for efficient diagnosis of coronary artery disease has been developed, implemented and evaluated. Hence, the system has archived 90.08% overall accuracy which is very excellent, thus the accuracy determines the proportion of the total number of predictions that were correct. At the same time, the system has 91.30% accuracy to classify of normal patients correctly by the system (specificity) and 90.24% accuracy to classify abnormal patients correctly by the system (sensitivity). This showed that, the system performed efficiently and excellently to diagnose CAD.
... Therefore, in order to improve the training speed and the accuracy of the model, it is necessary to extract effective rules. Referring to literature [17] and [18], and making a proper analysis of the dataset, 87 rules are adopted in this system. An example of these rules is shown below: ...
Cardiovascular disease remains the leading cause of death worldwide over the past two decades. Because of a large number of clinical data and the complexity of the disease, it is often challenging to diagnose and make the proper treatment. Over the past decade, as a soft computing method, fuzzy expert systems have been applied in disease diagnosis by many researches because of its superiority in dealing with uncertain and ambiguous problems. This study proposes an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to diagnose heart disease. Parameters related to membership functions in ANFIS are optimized by applying the genetic algorithm. The experiment was conducted on the public UCI heart disease datasets. The experimental result shows that 91.25% accuracy was obtained on the testing set, which was found to be satisfying based on comparison.
... Fuzzy expert systems are thus expert systems that help physicians resolve issues surrounding uncertainties, which could help improve physician decisions. [20][21][22][23][24] ...
Background:
Pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are common conditions affecting women's health and quality of life. In 50% of cases, SUI occurs after POP surgery, which is called de novo SUI. Predicting the risk of de novo SUI is a complex multi-attribute decision-making process. The current study made available a Decision Support System in the form of a fuzzy calculator web-based application to help surgeons predict the risk of de novo SUI.
Materials and methods:
We first identified 12 risk factors and the diagnostic criteria for de novo SUI by means of a systematic review of the literature. Then based upon an expert panel, all risk factors were prioritized. A set of 232 fuzzy rules for the prediction of de novo SUI was determined. A fuzzy expert system was developed using MATLAB software and Mamdani Inference System. The risk prediction model was then evaluated using retrospective data extracted from 30 randomly selected medical records of female patients over the age of 50 without symptoms of urinary incontinence who had undergone POP surgery. Finally, the proposed results of the predictive system were compared with the results of retrospective medical record data review.
Results:
The results of this online calculator show that the accuracy of this risk prediction model, at more than 90%, compared favorably to other SUI risk prediction models.
Conclusions:
A fuzzy logic-based clinical Decision Support System in the form of an online calculator for calculating SUI prognosis after POP surgery in women can be helpful in predicting de novo SUI.
