A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

2017 ◽  
pp. 293-325
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

Fuzzy Systems ◽  
2017 ◽  
pp. 682-714 ◽  
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

scholarly journals Diagnosis of Coronary Artery Disease using 1-D Convolutional Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 2959-2966 ◽  
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Convolutional Neural Network ◽  
Intelligent Systems ◽  
Early Stage ◽  
Second Phase ◽  
Data Set ◽  
Artery Disease

Heart disease is treated as one of the noxious diseases at the present time. Coronary artery disease is a kind of heart syndrome which is statistically growing day by day in the society. It is very tough for medical practitioners to predict Coronary artery disease as it is a complicated task that needs experience and acquaintance. For the detection of the disease doctors normally prescribe various invasive and non-invasive methods like angiography, ECG and echocardiogram. These methods are very expensive and sometimes not able to discover a number of undiagnosed symptoms. Due to these, it is not possible to detect the disease accurately at an early stage. The medical sector today contains a number of useful data that is helpful to detect a disease accurately. Using this data many researchers have proposed a number of intelligent systems for the detection of the disease. In this work, a competent system is implemented using deep learning for the better detection of the disease. The system is constructed using Convolutional Neural Network. The system has three phases. In the first phase data cleaning, data imputation and important feature selection are performed. In the second phase model training and hyperparameter tuning is performed. Finally, in the last phase, the model prediction is performed using the test data. The data set used for experimentation is Cleveland, Hungary, Switzerland and Long beach heart disease data present in the UCI repository. The proposed system gives a classification accuracy of 96.49% during testing, which is highest among all the discussed methods.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

An Optimized BP Neural Network Based on DS Evidential Reasoning on Heart Disease Prediction

2012 ◽  
Vol 263-266 ◽  
pp. 3342-3347
Author(s):  
Nan Nan Xie ◽  
Fei Yan Chen ◽  
Kuo Zhao ◽  
Liang Hu
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Evidential Reasoning ◽  
Self Organization ◽  
Uncertain Information ◽  
Data Set ◽  
Time Prediction ◽  
Minimum Points

BP neural network is a widely used neural network, with advantages as adaptability, fault tolerance and self-organization. However, BP neural network is difficult to determine the network structure, and easy to fall into local minimum points. In this paper, an optimized BP neural network was proposed based on DS, he advantages of DS Evidential Reasoning on uncertain information are used to improve the recognition rate and credibility of BP. Experiments on Heart Disease Data set shows the proposed method have good performance on run time, prediction accuracy and robustness.

Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm

2017 ◽  
Vol 141 ◽  
pp. 19-26 ◽  
Author(s):  
Zeinab Arabasadi ◽  
Roohallah Alizadehsani ◽  
Mohamad Roshanzamir ◽  
Hossein Moosaei ◽  
Ali Asghar Yarifard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Heart Disease ◽  
Disease Detection ◽  
Hybrid Neural Network ◽  
Computer Aided ◽  
Computer Aided Decision

scholarly journals Analysis of evaluation problems of the risk situation of patieents suffering from ischemic heart disease

2012 ◽  
Vol 60 (2) ◽  
pp. 125-134 ◽  
Author(s):  
Vladimír Konečný ◽  
Milan Sepši ◽  
Oldřich Trenz
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Complex Analysis ◽  
Partial Solution ◽  
Health Condition ◽  
Ischemic Heart ◽  
Classification Model ◽  
Data Set ◽  
Software Application

The ischemic heart disease represents a very common health issue which, thanks to its seriousness, impacts a big part of the population and is the cause of about one third of all death cases in the Czech Republic. For the analysis itself, data from medicinal practice of one of the authors of the article have been used and this study is a follow up of his PhD thesis. Concretely it was a set of patients which were being rehabilitated after a heart stroke; the results of the medical examination of these patients create 26 parameters. This data has been obtained in the course of the patients’ treatment. In the first phase of generating the classification model, the parameters that didn’t have a detrimental effect on the assessment of health condition of the patients have been removed from the data set and have been kept in the category of additional parameters. For the classification itself, an approach from artificial intelligence – applying a neural network - has been chosen. For the recording and transformation of the entering data a special application has been made. The classification and analysis of the data is performed on an experimental model of the self-learning of a neural network. The conclusions that arise from the initial analysis of this issue and the partial solution can be generalized and when using an appropriate software application they could even be used in medical practice. To do a complex analysis of the influence of all 26 parameters on the overall state of health of the patients is very difficult. A decision-making model appears to be a good solution. Last but not least, the proposed solution has to be verified on a bigger sample of patients afflicted by the ischemic heart disease.

scholarly journals Automated Heart Dysfunctionality Identification Based on Iris using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 528-531
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
The Body ◽  
Iris Localization ◽  
Human Iris ◽  
The World

One of the most deadly diseases in the world is Heart Disease. The dysfunctionality of the heart at the early stage can be detected using iridology. The study of iridology describes the structure of the human iris as an observation of the condition of organs in the body. In this article, we explore the heart condition through a series of stages such as iris localization, segmentation, extraction of region of interest, histogram equalization and classification using convolutional neural network. The results are evaluated using various quality metrics such as precision, recall, f-score & accuracy.

An Efficient Artificial Neural Network for Coronary Heart Disease Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1474-1483
Author(s):  
Priyam Vinay Sheta
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Heart Disease ◽  
Early Stage ◽  
Large Population ◽  
Input Feature ◽  
The Neural Network ◽  
Artificial Neural

Abstract: Coronary heart disease is rapidly increasing over these days also with a significant number of deaths. A large population around the world is suffering from the disease. When surveys were carried out of the death rate and the number of people suffering from the coronary heart disease, it was understood that how important is the diagnosis of this disease at an early stage. The old way for detecting the disease was not found effective. This paper suggests a different method and technology to detect the disease and the proposed method is more effective than the old traditional methods. In this paper, an artificial neural network that predicts the coronary heart disease is used with 14 features as the input. Feature selection, data preprocessing, and removing irrelevant data was done before training the neural network. The backpropagation algorithm was used for making the neural network learn the features. The output of data was basically binary but the neural network was trained to give the output as a probability between 0 and 1. Two algorithms were proposed for this prediction named Logistic Regression and Artificial Neural Network but the later was selected because of the accuracy of 94%. The accuracy of Logistic Regression was 87%.

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