Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression

Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure.Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed

Predictive Model for Likelihood of Detecting Chronic Kidney Failure and Disease Using Fuzzy Logic

Fuzzy logic is highly appropriate and valid basis for developing knowledge-based systems in medicine for different tasks and it has been known to produce highly accurate results. Examples of such tasks include syndrome differentiation, likelihood survival for sickle cell anaemia among paediatric patients, diagnosis and optimal selection of medical treatments and real time monitoring of patients. For this paper, a Fuzzy logic-based system is untaken used to provide a comprehensive simulation of a prediction model for determining the likelihood of detecting Chronic Kidney failure/diseases in humans. The Fuzzy-based system uses a 4-tuple record comprising of the following test taken: Blood Urea Test, Urea Clearance Test, Creatinine Clearance test and Estimated Glomerular Filtrate rate (eGFR). Understanding of the test was elicited from a private hospital in Ibadan through the help of an experienced and qualified nurse which also follows same test according to National Kidney Foundation. This knowledge was then used in the developing the simulated and rule-base prediction model using MATLAB software. The paper also follows the 3 major stages of Fuzzy logic. The results of fuzzification of variables, inference, model testing and defuzzification of variables was also presented. This in turn simplifies the complication involved in detecting Chronic Kidney failure/disease using Fuzzy logic based model. Keywords: Fuzzy logic, prediction model, likelihood, chronic kidney disease/failure DOI: 10.7176/JIEA/9-3-04 Publication date:May 31st 201

A Genetic-Neural System Diagnosing Hepatitis B

Hepatitis B is a life threaten disease and if not diagnose early can lead to death of the infected patient. In this paper a genetic neural system for diagnosing hepatitis B was designed. The system was designed to diagnose HBV using clinical symptoms. The dataset used in training the system was gotten from UCI repository. The system incorporated both genetic algorithm and neural network. The genetic algorithm was used to optimize the dataset used in training the neural network. The neural network was trained for 300 iterations and the system had a prediction accuracy of 99.14% on predicting Hepatitis B

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively

Artificial intelligence methodologies and their application to diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers?doctors and nurses?in this field

Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?

Markers used in everyday clinical practice cannot distinguish between the permanent impairment of renal function. Sodium and potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. The aim of this study was to investigate volemia and electrolyte status from a clinical cardiorenal viewpoint under consideration of renal function utilizing artificial intelligence. In this paper, an analysis of five variables: B-type natriuretic peptide, sodium, potassium, ejection fraction, EPI creatinine-cystatin C, was performed using an algorithm based on the adaptive neuro fuzzy inference system. B-type natriuretic peptide had the greatest influence on the ejection fraction. It has been shown that values of both Na+ and K+ lead to deterioration of the condition and vital endangerment of patients. To identify the risk of occurrence, the model identifies a prognostic biomarker by random regression from the total data set. The predictions obtained from this model can help optimize preventative strategies and intensive monitoring for patients identified as at risk for electrolyte disturbance and hypervolemia. This approach may be superior to the traditional diagnostic approach due to its contribution to more accurate and rapid diagnostic interpretation and better planning of further patient treatmen

Designing and Implementation of Fuzzy Case-based Reasoning System on Android Platform Using Electronic Discharge Summary of Patients with Chronic Kidney Diseases

Introduction: Case-based reasoning (CBR) systems are one of the effective methods to find the nearest solution to the current problems. These systems are used in various spheres as well as industry, business, and economy. The medical field is not an exception in this regard, and these systems are nowadays used in the various aspects of diagnosis and treatment. Methodology: In this study, the effective parameters were first extracted from the structured discharge summary prepared for patients with chronic kidney diseases based on data mining method. Then, through holding a meeting with experts in nephrology and using data mining methods, the weights of the parameters were extracted. Finally, fuzzy system has been employed in order to compare the similarities of current case and previous cases, and the system was implemented on the Android platform. Discussion: The data on electronic discharge records of patients with chronic kidney diseases were entered into the system. The measure of similarity was assessed using the algorithm provided in the system, and then compared with other known methods in CBR systems. Conclusion: Developing Clinical fuzzy CBR system used in Knowledge management framework for registering specific therapeutic methods , Knowledge sharing environment for experts in a specific domain and Powerful tools at the point of care

Fuzzy logic: A “simple” solution for complexities in neurosciences?

Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences

Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients

Dengue disease is considered as one of the life threatening disease that has no vaccine to reduce its case fatality. In clinical practice the case fatality of dengue disease can be reduced to 1 if the dengue patients are hospitalized and prompt intravenous fluid therapy is administrated. Yet, it has been a great challenge to the physicians to decide whether to hospitalize the dengue patients or not due to the overlapping of the medical diagnosis criteria of the disease. Beside that physicians cannot decide to admit all patients because this will have major impact on health care cost saving due to the huge incident of dengue disease in the country. Even if the physicians managed to identify the critical cases to be hospitalized, most of the tools that have been used for monitoring those patients are invasive. Therefore, this study was conducted to develop a non-invasive accurate diagnostic system that can assist the physicians to diagnose the risk in dengue patients and therefore attain the correct decision. Bioelectrical Impedance Analysis measurements, Symptoms and Signs presented with dengue patients were incorporated with Adaptive Neuro-Fuzzy Inference System (ANFIS) to construct two diagnostic models. The first model was developed by systematically optimizing the initial ANFIS model parameters while the second model was developed by employing the subtractive clustering algorithm to optimize the initial ANFIS model parameters. The results showed that the ANFIS model based on subtractive clustering technique has superior performance compared with the other model. Overall diagnostic accuracy of the proposed system is 86.13 with 87.5 sensitivity and 86.7 specificity. © 2011 Elsevier Ltd. All rights reserved