Embedded System for Heart Disease Recognition using Fuzzy Clustering and Correlation

2012 ◽  
pp. 327-350
Author(s):  
Helton Hugo de Carvalho Júnior ◽  
Robson Luiz Moreno ◽  
Tales Cleber Pimenta
Keyword(s):  
Heart Disease ◽  
Embedded System ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Heart Diseases ◽  
Significant Loss ◽  
Data Set ◽  
Viability Analysis ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

This chapter presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram – ECG signal processing by reducing the amount of data samples without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson-Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicates common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database – EDB as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan®-3A FPGA. The FPGA implemented a Xilinx Microblaze® Soft-Core Processor running at a 50 MHz clock rate.

The Application of Fuzzy Clustering Number Algorithm in Network Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 2220-2223
Author(s):  
Lang Guo
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Cluster Number ◽  
Data Set ◽  
Network Intrusion ◽  
Correlation Degree ◽  
Indicator Data ◽  
Detection Effect

In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.

The abnormal ECG without apparent heart disease and the normal ECG in serious heart diseases: two extremes

ESC CardioMed ◽  
2018 ◽  
pp. 391-393
Author(s):  
Antonio Bayés de Luna ◽  
Günter Breithardt
Keyword(s):  
Heart Disease ◽  
Cardiovascular Health ◽  
Heart Diseases ◽  
Clinical Context ◽  
Electrocardiogram Ecg

It is clear that the electrocardiogram (ECG) must be evaluated within the clinical context. Therefore, this chapter can be summarized with the following sentence: a normal ECG is not a guarantee of cardiovascular health, nor is a pathological ECG an unequivocal sign of heart disease. This must always be remembered when interpreting an ECG.

Introduction

ESC CardioMed ◽  
2018 ◽  
pp. 337-338
Author(s):  
Antonio Bayés de Luna
Keyword(s):  
Heart Disease ◽  
Heart Diseases ◽  
Medical Supervision ◽  
Automatic Interpretation ◽  
Great Relevance ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) is one of the simplest, inexpensive, safest, and reproducible techniques that exist in medicine. During the current renaissance of electrocardiography, we need to improve how to interpret an ECG. This is especially important because currently the automatic interpretation still needs medical supervision. In spite of the great relevance of the ECG in the diagnosis of heart diseases, this section also includes a chapter that expresses the limitations of the ECG, entitled ‘The abnormal ECG without apparent heart disease and the normal ECG in serious heart disease: two extremes’.

Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means

2011 ◽  
Vol 219-220 ◽  
pp. 1263-1266
Author(s):  
Xi Huai Wang ◽  
Jian Mei Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Soft Sensor ◽  
Fuzzy Cluster ◽  
Training Data ◽  
Temperature Estimation ◽  
Data Set ◽  
Slab Temperature ◽  
Walking Beam Reheating Furnace

A neural network soft sensor based on fuzzy clustering is presented. The training data set is separated into several clusters with different centers, the number of fuzzy cluster is decided automatically, and the clustering centers are modified using an adaptive fuzzy clustering algorithm in the online stage. The proposed approach has been applied to the slab temperature estimation in a practical walking beam reheating furnace. Simulation results show that the approach is effective.

scholarly journals Identification and Implementation Perspective on the Stratification Algorithms in the Prognostication of Heart Disease using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1223-1225
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Diseases ◽  
Classification Algorithm ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Combination Of Classifiers ◽  
Great Idea

Analysis of patient’s data is always a great idea to get accurate results on using classifiers. A combination of classifiers would give an accurate result than using a single classifier because one single classifier does not give accurate results but always appropriate ones. The aim is to predict the outcome feature of the data set. The “outcome” can contain only two values that is 0 and 1. 0 means patient doesn’t have heart disease and 1 means patient have heart diseases. So, there is a need to build a classification algorithm that can predict the Outcome feature of the test dataset with good accuracy. For this understanding the data is important, and then various classification algorithm can be tested. Then the best model can be selected which gives highest accuracy among all. The built model can then be given to the software developer for building the end user application using the selected machine learning model that will be able to predict the heart disease in a patient.

scholarly journals Heart Disease Prediction

2018 ◽  
Vol 7 (3.12) ◽  
pp. 750
Author(s):  
S Vinothini ◽  
Ishaan Singh ◽  
Sujaya Pradhan ◽  
Vipul Sharma
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Logistics Regression ◽  
Learning Technique ◽  
Work Integration

Machine learning algorithm are used to produce new pattern from compound data set. To cluster the patient heart condition to check whether his /her heart normal or stressed or highly stressed k-means clustering algorithm is applied on the patient dataset. From  the results of clustering ,it is hard to elucidate and to obtain the required conclusion from these clusters. Hence another algorithm, the decision tree, is used for the exposition of the clusters of . In this work, integration of decision tree with the help of k-means algorithm is aimed. Another learning technique such as SVM and Logistics regression is used. Heart disease prediction results from SVM and Logistics regression were compared. 

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 1244-1250
Author(s):  
Ting Ting Zhao
Keyword(s):  
Data Mining ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Data Set ◽  
Fuzzy Similarity

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

The abnormal ECG without apparent heart disease and the normal ECG in serious heart diseases: two extremes

ESC CardioMed ◽  
2018 ◽  
pp. 391-393
Author(s):  
Antonio Bayés de Luna ◽  
Günter Breithardt
Keyword(s):  
Heart Disease ◽  
Cardiovascular Health ◽  
Heart Diseases ◽  
Clinical Context ◽  
Electrocardiogram Ecg

It is clear that the electrocardiogram (ECG) must be evaluated within the clinical context. Therefore, this chapter can be summarized with the following sentence: a normal ECG is not a guarantee of cardiovascular health, nor is a pathological ECG an unequivocal sign of heart disease. This must always be remembered when interpreting an ECG.

Different Approaches for Missing Data Handling in Fuzzy Clustering: A Review

2020 ◽  
Vol 13 (6) ◽  
pp. 833-846
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Missing Data ◽  
Fuzzy Clustering ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Linear Interpolation ◽  
Performance Criteria ◽  
Data Sets ◽  
Data Set ◽  
Fcm Clustering ◽  
Missing Attributes

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem in many research areas. The reasons for the lack of missing attributes may be several; human error in tabulating/recording the data, machine failure, errors in data acquisition or refusal of a patient/customer to answer few questions in a questionnaire or survey. Further, clustering of such data sets becomes a challenge. Objective: In this paper, we presented a critical review of various methodologies proposed for handling missing data in clustering. The focus of this paper is the comparison of various imputation techniques based FCM clustering and the four clustering strategies proposed by Hathway and Bezdek. Methods: In this paper, we imputed the missing values in incomplete datasets by various imputation/ non-imputation techniques to complete the data set and then conventional fuzzy clustering algorithm is applied to get the clustering results. Results: Experiments on various synthetic data sets and real data sets from UCI repository are carried out. To evaluate the performance of the various imputation/ non-imputation based FCM clustering algorithm, several performance criteria and statistical tests are considered. Experimental results on various data sets show that the linear interpolation based FCM clustering performs significantly better than other imputation as well as non-imputation techniques. Conclusion: It is concluded that the clustering algorithm is data specific, no clustering technique can give good results on all data sets. It depends upon both the data type and the percentage of missing attributes in the dataset. Through this study, we have shown that the linear interpolation based FCM clustering algorithm can be used effectively for clustering of incomplete data set.

scholarly journals Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

2021 ◽  
pp. 036119812110107
Author(s):  
Naghmeh Niroomand ◽  
Christian Bach ◽  
Miriam Elser
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Real Data ◽  
Superior Performance ◽  
Clustering Methods ◽  
Data Set ◽  
Passenger Car ◽  
Continuous Growth ◽  
Taxation Policy ◽  
Fuzzy Clustering Algorithm

There has been globally continuous growth in passenger car sizes and types over the past few decades. To assess the development of vehicular specifications in this context and to evaluate changes in powertrain technologies depending on surrounding frame conditions, such as charging stations and vehicle taxation policy, we need a detailed understanding of the vehicle fleet composition. This paper aims therefore to introduce a novel mathematical approach to segment passenger vehicles based on dimensions features using a means fuzzy clustering algorithm, Fuzzy C-means (FCM), and a non-fuzzy clustering algorithm, K-means (KM). We analyze the performance of the proposed algorithms and compare them with Swiss expert segmentation. Experiments on the real data sets demonstrate that the FCM classifier has better correlation with the expert segmentation than KM. Furthermore, the outputs from FCM with five clusters show that the proposed algorithm has a superior performance for accurate vehicle categorization because of its capacity to recognize and consolidate dimension attributes from the unsupervised data set. Its performance in categorizing vehicles was promising with an average accuracy rate of 79% and an average positive predictive value of 75%.

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