Automated diagnosis of coronary heart disease using neuro-fuzzy integrated system

Risk Level Prediction of Chronic Kidney Disease Using Neuro- Fuzzy and Hierarchical Clustering Algorithm (s)

“…In their research they compared three techniques which include artificial neural networks (ANN), Naïve Bayes and Decision trees using WEKA tool. Naganna Chetty et al, [11] in their research, the Role of Attributes Selection in Classification of Chronic Kidney Disease Patients, classification models with different classification algorithms which included wrapper subset and best first search method were explored in order to predict and classify CKD and non CKD patients. From their results it was evident that they obtained better accuracy on a dataset where they applied feature selection on the dataset as compared to when they did not apply any attribute selection mechanism.…”

A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

“…The attributes of both methods are divided and given to the two neural network models, Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The results of the two techniques were integrated and provided the final training accuracy and testing accuracy Ansari & Gupta [23] proposed a computational intelligence technique which combined fuzzy systems, neural network and evolutionary computing for the diagnosis of coronary heart disease. In order to show the effectiveness of the proposed system, Simulation for automated diagnosis was performed using the realistic causes of coronary heart disease.…”

Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review

“…al. [21] performed a work, "Automated Diagnosis of Coronary Heart Disease Using Neuro-Fuzzy Integrated System". In this paper presented, computational intelligence combines fuzzy systems, neural network and evolutionary compute and focused Neurofuzzy integrated system for coronary heart disease.…”