OBJECTIVE: This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery. METHODS: A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia. RESULTS: Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%. CONCLUSION The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.
Acupuncture Points ; Acupuncture Therapy ; Fuzzy Logic

Risks exist in medicine related fields, which cannot be defined and quantified precisely. It is necessary to adopt a method for the risk assessment of uncertain and fuzzy phenomenon. This paper summarizes the thinking, procedure, advantage and application of fuzzy analytic hierarchy process in the risk assessment in medicine related fields for the purpose of providing reference for its further application.
Analytic Hierarchy Process ; Fuzzy Logic ; Humans ; Risk Assessment/methods*

OBJECTIVE: A method for dynamically collecting and processing ECG signals was designed to obtain classification information of abnormal ECG signals. METHODS: Firstly, the ECG eigenvectors were acquired by real-time acquisition of ECG signals combined with discrete wavelet transform, and then the ECG fuzzy information entropy was calculated. Finally, the Euclidean distance was used to obtain the semantic distance of ECG signals, and the classification information of abnormal signals was obtained. RESULTS: The device could effectively identify abnormal ECG signals on an embedded platform based on the Internet of Things, and improved the diagnosis accuracy of heart diseases. CONCLUSIONS The fuzzy diagnosis device of ECG signal could accurately classify the abnormal signal and output an online signal classification matrix with a high confidence interval.
Algorithms ; Arrhythmias, Cardiac ; Electrocardiography ; Fuzzy Logic ; Heart Diseases ; diagnosis ; Humans ; Internet ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Based on the noninvasive detection indeices and fuzzy mathematics method, this paper studied the noninvasive, convenient and economical cardiovascular health assessment system. The health evaluation index of cardiovascular function was built based on the internationally recognized risk factors of cardiovascular disease and the noninvasive detection index. The weight of 12 indexes was completed by the analytic hierarchy process, and the consistency test was passed. The membership function, evaluation matrix and evaluation model were built by fuzzy mathematics. The introducted methods enhanced the scientificity of the evaluation system. Through the Kappa consistency test, McNemer statistical results ( = 0.995 > 0.05) and Kappa values (Kappa = 0.616, < 0.001) suggest that the comprehensive evaluation results of model in this paper are relatively consistent with the clinical, which is of certain scientific significance for the early detection of cardiovascular diseases.
Cardiovascular Diseases ; diagnosis ; Cardiovascular System ; Fuzzy Logic ; Humans ; Models, Cardiovascular ; Research

BACKGROUND: Disease diagnosis is complicated since patients may demonstrate similar symptoms but physician may diagnose different diseases. There are a few number of investigations aimed to create a fuzzy expert system, as a computer aided system for disease diagnosis. METHODS: In this research, a cross-sectional descriptive study conducted in a kidney clinic in Tehran, Iran in 2012. Medical diagnosis fuzzy rules applied, and a set of symptoms related to the set of considered diseases defined. The input case to be diagnosed defined by assigning a fuzzy value to each symptom and then three physicians asked about each suspected diseases. Then comments of those three physicians summarized for each disease. The fuzzy inference applied to obtain a decision fuzzy set for each disease, and crisp decision values attained to determine the certainty of existence for each disease. RESULTS: Results indicated that, in the diagnosis of seven cases of kidney disease by examining 21 indicators using fuzzy expert system, kidney stone disease with 63% certainty was the most probable, renal tubular was at the lowest level with 15%, and other kidney diseases were at the other levels. The most remarkable finding of this study was that results of kidney disease diagnosis (e.g., kidney stone) via fuzzy expert system were fully compatible with those of kidney physicians. CONCLUSION: The proposed fuzzy expert system is a valid, reliable, and flexible instrument to diagnose several typical input cases. The developed system decreases the effort of initial physical checking and manual feeding of input symptoms.
Diagnosis ; Expert Systems ; Fuzzy Logic ; Humans ; Iran ; Kidney Calculi ; Kidney Diseases* ; Kidney*

OBJECTIVES: Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. METHODS: We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. RESULTS: Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management. CONCLUSIONS: Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.
Artificial Intelligence ; Data Collection ; Data Mining* ; Decision Trees ; Delivery of Health Care ; Early Diagnosis ; Education ; Follow-Up Studies ; Fuzzy Logic ; Humans ; Information Systems* ; Mass Screening ; Methods ; Mobile Applications ; Patient Care ; Self Care ; Smartphone ; Statistics as Topic ; Support Vector Machine ; Telemedicine

OBJECTIVETo investigate the chemical transformation of volatile compounds in sulfur-fumigated Radix Angelicae Sinensis.

METHODSA comprehensive two-dimensional gas chromatography (GC×GC) and high-resolution time-of-flight mass spectrometry (HR-TOF/MS) with colorized fuzzy difference (CFD) method was used to investigate the effect of sulfur-fumigation on the volatile components from Radix Angelicae Sinensis.

RESULTSTwenty-five compounds that were found in sun-dried samples disappeared in sulfur-fumigated samples. Seventeen volatile components including two sulfur-containing compounds were newly generated for the first time in volatile oils of sulfur-fumigated Radix Angelicae Sinensis.

CONCLUSIONThe strategy can be successfully applied to rapidly and holistically discriminate sun-dried and sulfur-fumigated Radix Angelicae Sinensis. GC×GC-HR-TOF/MS based CFD is a powerful and feasible approach for the global quality evaluation of Radix Angelicae Sinensis as well as other herbal medicines.

Color ; Drugs, Chinese Herbal ; analysis ; Fumigation ; Fuzzy Logic ; Gas Chromatography-Mass Spectrometry ; methods ; Oils, Volatile ; analysis ; Reference Standards ; Sulfur ; analysis ; Volatile Organic Compounds ; analysis

OBJECTIVETo review theories and technologies of big data mining and their application in clinical medicine.

DATA SOURCESLiteratures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015.

STUDY SELECTIONOriginal articles regarding big data mining theory/technology and big data mining's application in the medical field were selected.

RESULTSThis review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine.

CONCLUSIONBig data mining has the potential to play an important role in clinical medicine.

Bayes Theorem ; Clinical Medicine ; Data Mining ; Decision Support Systems, Clinical ; Decision Trees ; Evidence-Based Medicine ; Fuzzy Logic ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated

OBJECTIVES: The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. METHODS: A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. RESULTS: The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. CONCLUSIONS: The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.
Classification ; Coronary Disease* ; Data Mining ; Dataset ; Decision Trees* ; Fuzzy Logic* ; Heart Diseases ; Korea ; Nutrition Surveys ; ROC Curve ; Uncertainty

This paper aims to investigate whether good governance of a recipient country is a necessary condition and what combinations of factors including governance factor are sufficient for low prevalence of HIV/AIDS in HIV/AIDS aid recipient countries during the period of 2002-2010. For this, Fuzzy-set Qualitative Comparative Analysis (QCA) was used. Nine potential attributes for a causal configuration for low HIV/AIDS prevalence were identified through a review of previous studies. For each factor, full membership, full non-membership, and crossover point were specified using both author's knowledge and statistical information of the variables. Calibration and conversion to a fuzzy-set score were conducted using Fs/QCA 2.0 and probabilistic tests for necessary and sufficiency were performed by STATA 11. The result suggested that governance is the necessary condition for low prevalence of HIV/AIDS in a recipient country. From sufficiency test, two pathways were resulted. The low level of governance can lead to low level of HIV/AIDS prevalence when it is combined with other favorable factors, especially, low economic inequality, high economic development and high health expenditure. However, strengthening governance is a more practical measure to keep low prevalence of HIV/AIDS because it is hard to achieve both economic development and economic quality. This study highlights that a comprehensive policy measure is the key for achieving low prevalence of HIV/AIDS in recipient country.
Acquired Immunodeficiency Syndrome/*epidemiology/prevention & control ; Computer Simulation ; Developing Countries/*economics/statistics & numerical data ; Economic Development/statistics & numerical data ; Fraud/economics/*statistics & numerical data ; Fuzzy Logic ; HIV Infections/*epidemiology/prevention & control ; Humans ; Models, Statistical ; Prevalence ; Risk Factors ; Socioeconomic Factors