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Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

In phase 4, the neural network model using the parameters obtain in phase 1,2 and 3 will be simulated numerous times. Every iteration will be flagged with an “iteration identification” in order to find which iteration produce the best accuracy. Weight and bias for every iteration also is stored and flagged with “iteration identification”. After all iteration in phase 4 is completed, the algorithm will sort the results according to the validation dataset classification accuracy. The iteration identification will determine which iteration produce the highest accuracy of classification. The weight and bias of iteration with the highest classification will be extracted from the array. The neural network model with optimize parameters and the best weight and bias is simulated using test dataset for evaluation. The proposed framework uses two sets of heart disease data taken from UCI machine learning data repository. The Statlog dataset and Cleveland Heart dataset is used to evaluate the performance of proposed framework. The result of simulation then compared to the reported results published by the previous research. Dataset is partition into ratio of 80% for training, 10% for validation and 10% for testing.
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Developing Resource Efficient Heart Arrhythmia Classifier

Developing Resource Efficient Heart Arrhythmia Classifier

This paper presents development of optimal digital circuit for the Heart Disease Classification using Cartesian Genetic Programming (CGP) for different types of arrhythmia. Extensive research work has already been carried out in this domain but non-linear nature of the technique remained one of the hurdles in its hardware prototyping. Efficient circuit development for resource constraint environment of the classifier remained an unsolved problem due to its algorithmic complexity. CGP system is trained to generate a classifier circuit based upon the fiducial points extracted out of the Electrocardiography (ECG) signals of dataset. Experimental results reported on heart disease data from machine learning repository of MIT-BIH showed satisfactory results as compare to other contemporary methods used in the field..
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Title: Diagnostic the Heart Valve Diseases using Eigen Vectors

Title: Diagnostic the Heart Valve Diseases using Eigen Vectors

Abstract ــــ Heart disease is cause more health problem to the humane and may be lead of death. Then in this paper, we propose novel techniques for heart disease classification using Eigenvector technique and feature parameter extraction based on MFCC. Eigenvector approach is seemed to be an adequate method to be used in recognition due to its simplicity, speed and learning capability. In this research we achieve low computational overhead for the feature recognition stage since we use only 11 weighted MFCC. The effectiveness of proposed techniques leading to higher recognition accuracy of 86%.
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Performances Analysis of Heart Disease Dataset using Different Data Mining Classifications

Performances Analysis of Heart Disease Dataset using Different Data Mining Classifications

Several different research groups have been studied that related to heart diseases using classifications techniques [1]– [4]. The studies that have been done were included the study of heart disease classification using many classifications algorithm. In this research, we have used five classification techniques. Classification is the process of finding a set of models that differentiate the data by telling the classes and meaning behind the data. The models are used to predict the class whose label is unknown. Famous classification algorithm such as decision trees, neural networks, Naïve Bayes, and SMO are being studied and further compare the usability of this research. In this research, we focused on heart disease classification using five classifiers.
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Decision Support System for Disease Prediction Using Clustering

Decision Support System for Disease Prediction Using Clustering

In 2017 Sanjay Kumar Sen proposed “Predicting and Diagnosing of Heart Disease Using Machine Learning Algorithms”. The main objective of this research is predicting the heart disease of a patient using machine learning algorithms. Comparative study of the various performances of machine learning algorithms is done through graphical representation of the results. They carried out an experiment to find the predictive performance of different classifiers. They select four popular classifiers considering their qualitative performance for the experiment. They also choose one dataset from heart available at UCI machine learning repository. Naïve base classifier is the best in performance. In order to compare the classification performance of four machine learning algorithms, classifiers are applied on same data and results are compared on the basis of misclassification and correct classification rate and according to experimental results it can be concluded that Naïve base classifier is the best as compared to Support Vector Machine, Decision Tree and K-Nearest Neighbour[7]. In 2018 Poornima V, Gladis D proposed “A novel approach for diagnosing heart disease with hybrid classifier”. They proposed an Orthogonal Local Preserving Projection (OLPP) method to reduce the function dimension of the input high-dimensional data. The dimension reduction improves the prediction rate with the help of hybrid classifier i.e. Group Search Optimization Algorithm (GSO) combine with the Levenberg-Marquardt (LM) training algorithm in the neural network. The LM training algorithm is used to solve the optimization problem and it determines the best network parameters such as weights and bias that minimizes the error. The final output of the optimization technique is combined with the performance metrics as accuracy, sensitivity, and specificity. From the result, it is observed that hybrid optimization techniques increase the accuracy of the heart disease prediction system[8].
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HCR-PSO Feature Selection for Heart Disease Prediction

HCR-PSO Feature Selection for Heart Disease Prediction

Data mining is a process of finding previous applied unknown patterns and trends in databases. This pattern is further used to build predictive models. In this paper, the main objective is to study various data mining techniques/algorithms that are used in the forecasting of heart diseases by some data mining tool. Our life depends upon the efficient working of heart, so it is the most vital part of our body. There may be a huge reasons for heart diseases like high blood pressure, high cholesterol, unhealthy diet, smoking, irregular exercises and obesity. Data mining techniques are used in many fields especially in health care industry. Usually in health care industry, huge amount of data which are complex is generated and this data is about medicines, hospital resources, patients, medical devices, disease diagnosis etc. This complex data needs to be analyzed and processed for the extraction process which then helps in taking decisions and is also cost effective. According to world health organization, there were 17.5 million people losing their life due to cardio vascular diseases in 2012, represents 31 percent of global deaths. Due to the coronary heart disease several million (approximately 7.4) people were died and due to stroke around 6.7 million were died as per estimation. By the year 2030, around 23.6 million people will lose their life due to heart disease as stated. Thus, a best method to predict the heart diseases is for efficient heart disease prediction system. This system will find human interpretable patterns and will determine trends in patient records to enhance health care.
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Prevalence of cardiovascular disease among Bangladeshi adult population: a systematic review and meta-analysis of the studies

Prevalence of cardiovascular disease among Bangladeshi adult population: a systematic review and meta-analysis of the studies

than those who live in rural areas. Urban residents have a more sedentary lifestyle than rural residents, who are often involved with heavy physical activity, and this difference contributes significantly to CVD prevalence. Differences in food supply and consumption patterns in urban areas and limited food diversity in rural areas influence dietary patterns and thus the status of CVD prevalence. Furthermore, urban residents are exposed to different environmental conditions (eg, access to physical activity facilities, better transportation, lack of open spaces and exposure to air pollution) than those who reside in rural areas. In general, the prevalence of CVD in Bangladesh is on the rise. The underlying factors behind the increased prevalence of CVD in the Bangladeshi population could be many. However, a geographical change in disease patterns from communicable to noncommunicable diseases, a growing trend of urbanization and an attraction for following a Western lifestyle could influence such increased CVD prevalence. 45,46
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Heart Disease Prediction Method using Hybrid Classifier

Heart Disease Prediction Method using Hybrid Classifier

3. Model Building and Prediction Analysis: - In the last phase, the input dataset will be divided into training and test phase. The training set will be more than 50 percent and rest of the part will be the test set. The dataset will be trained using the decision tree classification and final prediction is generated of the test set. The decision is hierarchical data structures which represents the data using a divide and conquer strategy is called decision tree. The categorical labels are used instead of non-parametric classification for discussing the decision trees. They can also be used to perform regression. Determining the labels for new examples is the aim of decision tree within classification. The instances are represented as feature vectors in the decision tree classifiers. The tests for feature values are denoted as nodes, the labels as leaves and for each value of feature at every node, one branch must be available. Entropy is used as a measure to define the information gain in this classifier. The impurity level of an arbitrary collection of examples is defined by entropy. For instance, if a collection S is considered which includes both positive and negative examples of any target set, the entropy is defined as:
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A Review on Data Mining Techniques for Heart Disease Prediction

A Review on Data Mining Techniques for Heart Disease Prediction

commonly proposed algorithms so far engaged data mining classification techniques such as Decision Tree, Artificial Neural Network, Linear Discriminant Analysis, Decision Tree and K-Nearest Neighbour ([AA15]). There is need to identify the performance of each classification method. To handle this work, it is therefore necessary to conduct a comparative performance evaluation on different classification techniques in data mining to reveal the accuracy of each classification techniques. A number of comparative performance analysis of data mining techniques have been done for the prediction of heart disease. The most existing comparative performance evaluation for heart disease prediction with different classification methods only validate using a dataset with just only one data mining software tool ([DA12]). In future work, two or more datasets (Cleveland Heart Disease & Statlog Heart Disease Database) and data mining software tools like Weka, Rapid Miner, Matlab, Orange and Tanagra ([KK16]) should be applied to substantiate and also produce comprehensive comparative performance evaluation for a heart disease predictive system.
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Analysis of Heart Disease Prediction System using Data Mining Techniques

Analysis of Heart Disease Prediction System using Data Mining Techniques

Now day’s Heart disease is main reason for death in the world. Heart disease is leading cause of death in the world [3]. Some of the major reason for heart diseases are blood pressure, cholesterol, pulse rate. More common type of heart disease that makes the heart works abnormally are congenital heart disease, heart failure, hypertensive heart disease, cardiomyopathy, heart murmurs, rheumatic heart disease, pulmonary stenosis and coronary artery disease. Heart can be attacked by various diseases that leads the heart to not work properly. The World Health Organization (WHO) has estimated that 12 million deaths around the world occurs each year from heart disease. In 2008, 17.3 million people died from heart disease. "Death rate increases by 80% in the world due to heart disease" The World Health Organization (WHO) estimates that by 2030, 23.6 million people worldwide will die from heart disease [4]. When the prediction of heart disease is getting accurate, then the specific patient can take preventive measures so that heart disease as the number one killer in the world can be reduced. The aim of the paper is to study the various data mining techniques using different tools. The algorithms are selected based on the accuracy. The risk level is classified by using SVM, KNN, Random forest and K star algorithms. This paper helps to understand the methodologies in the recent literature for predicting the heart disease using data mining techniques.
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Chromosome microarray analysis in the investigation of children with congenital heart disease

Chromosome microarray analysis in the investigation of children with congenital heart disease

In this study, PCNVs were detected in 31.1% (19/61) children with simple CHD and 23.2% (10/43) children with complex CHD by CMA. There was no significant difference between the two groups (P > 0.05). Detection rates in various types of CHD were different. Shaffer et al. reviewed 580 fetuses with CHD and normal karyotype by aCGH [25], and revealed the detection rates of PCNVs as follows: 16.2% (11/68) in LVOTO, 11.6% (5/ 43) in conotruncal defect and 10.6% (14/132) in septal defect. The above three types of CHD in fetuses were the most frequent. In our study, the PCNVs detection rates in different types of CHD in isolated or with add- itional anomalies were as follows in turn: 75% (3/4) in LVOTO + RVOTO, 45.5% (5/11) in RVOTO, 35.7% (5/ 14) in LVOTO, 33.3% (4/12) in PDA, 23.3% (10/43) in septal defect, 12.5% (2/16) in conotruncal defects. Our data demonstrate that LVOTO and/or RVOTO were most probably related to microdeletion/microduplica- tion. Of the 29 children with PCNVs, 22 (75.9%) were complicated with MCA and/or DD/ID. High detection Table 2 Classification of children with CHD and/or other diagnoses (MCA, ID/DD)
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Implementation Of An Efficient Hybrid Classification Model For Heart Disease Prediction

Implementation Of An Efficient Hybrid Classification Model For Heart Disease Prediction

Figure 2 represents the flow chart for hybrid method. SVM classifier is applied to classify the data in the linear hyper plane to map the attributes on each other. Classified data is then applied on k-NN data mining techniques to trained data for prediction. Results are analyzed in terms of precision, recall and accuracy. The Dataset is the multi variant in type which is preprocessed for the prediction analysis in the step one. In step two the input dataset is divided into training and testing data. The hybrid classification model which is the combination of SVM and k-NN is applied for the prediction analysis in step three. In the last step, two parameters which are accuracy and execution time are used for the performance analysis. The formula of the accuracy and execution time is explained below:
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Radiation-induced heart disease: a review of classification, mechanism and prevention

Radiation-induced heart disease: a review of classification, mechanism and prevention

types of cells in the heart after exposure to radiation, among which mitochondrial dysfunction and irreversible damage are the key links of cell apoptosis and necrosis, and the occurrence of mitochondrial dysfunction is closely related to endoplasmic reticulum(ER)stress. Mitochondria are organelles that account for an important proportion of the total volume of cardiac myocytes, and mitochondria carry extranuclear DNA, so they are important targets for radiation-induced cell damage[78]. Mitochondrial permeability transition (MPT) and loss of mitochondrial membrane potential are important mechanisms of mitochondrial dysfunction and are involved in the pathogenesis of a variety of cardiovascular diseases[79]. Multiple stimuli, such as calcium ions flowing into mitochondria, inorganic phosphates, reactive oxygen species and other oxidants, can also induce MPT[80]. After cardiac myocytes are irradiated, the stimulated ER releases calcium ions from the calcium pool of the ER into the cytoplasm. This process will cause mitochondrial calcium overload and lead to its membrane swelling and release of apoptotic factors from it. Moreover, severe MPT can lead to mitochondrial membrane depolarization and the decoupling of oxidative phosphorylation, which is closely related to the opening of mitochondrial permeability transition pore(mPTP)[39, 81]. Bax is one of the important pro-apoptotic proteins in the Bcl-2 family [82]. It has been reported that exposure to RT leads to increased expression and activation of Bax, leading to its translocation and insertion into the mitochondrial outer membrane [83, 84]. This accelerated the opening of mitochondrial voltage - dependent anion channels. MPT and the insertion and ectopia increase of Bax improve the permeability of mitochondrial membrane and reduce the mitochondrial membrane potential together. This prolongs and enhances calcium-induced mitochondrial membrane swelling, leading to apoptosis[71].
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An Exploration Of Prediction Of Heart Disease Using Machine Learning Classification

An Exploration Of Prediction Of Heart Disease Using Machine Learning Classification

sugar level. The algorithms like Naïve Bayes, SVM, and PCA has been used. The tool call WEKA is used. The comparison of heart disease and Diabetic Disease is done with the help of Graph. For the heart disease, the data set is divided into the figure of intervals of size 50. The dataset is then divided into training and testing data set. The comparison of Naïve Bayes before and after is calculated. M. Deepika Et al. [4] gave an outlook on all the kinds of disease and implemented them by the artificial neural network, naïve Bayes, decision tree, SVM and the comparison of the different disease like Heart Disease, Thyroid Disease, Diabetes, and Breast Cancer are shown in a Graph. Few challenges involve using a lot of parameters, the absolute probability is Unanswered. The important challenge is to develop a highly accurate and Efficient Model. It helps in finding examples and connections in the dataset. The client needs to think about the activities of the devices and the calculations -with the goal that they can investigate as an ideal technique from the delivered outcomes. The choice of information mining apparatus and the enhancement calculation will demonstrate an effect on speed and precision. I Ketut Agung Enriko Et al. [5] used Nearest neighbor search, naïve Bayes, and support vector machine by 14 attributes which include id, sex, age, symptoms, additional symptoms, blood pressure, heart rate, ECG, cholesterol, trop t, ckmb, GDP, GDS, diagnosis. The data set used in the research is collected from Harapan Kita hospital (HKH) which was located in Jakarta. It consists of 450 records and 36 types of diseases in which 29 of them are related to heart and 7 of them are not related to heart disease. Here the data preprocessing is done, it removes unwanted or unused data
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Diagnosis and Prediction of Health Diseases Using Data Mining Techniques

Diagnosis and Prediction of Health Diseases Using Data Mining Techniques

Ansari et. al. [1] performed a work," Automated Diagnosis of Coronary Heart Disease Using Neuro- Fuzzy Integrated System" explained fuzzy integrated system in neuroscience for the analysis of heart diseases.For the effectiveness of the projected system, Simulation is performed for computerized diagnosis by means of realistic causes of coronary heart disease. The author predicted that this system is suitable for the identification of patients with high/low cardiac risk. Subbalakshmi et. al. [2] performed a work "Decision Support in Heart Disease Prediction System using Naive Bayes” that published in year 2012. Shouman et. al. [3] performed a work “Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients” explains the detailed work that applied k- nearest neighbour on some kind of Heart Disease dataset to investigate proficiency and prediction of heart disease. Vijiyarani et. al. [4] performed a work, “An Efficient Classification Tree Technique for Heart Disease Prediction” analyzes the classified tree techniques in data mining. The classified tree algorithms that are used and tested in this work are Decision Stump, Random Forest, and LMT Tree algorithm. Rizwan Beg et. al. [5] performed a work "Data Mining in Clinical Decision Support Systems for Diagnosis, Prediction and Treatment of Heart Disease” which concluded that there is abundance of data available in medical institutions, but s t i l l data is not properly used yet. Shukla et. al. [6] performed a work “A Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro- Fuzzy Integrated Approach Two Level” designed a system which identifies the chances of a coronary heart disease. The main objective of this research is to use Naïve Bayes algorithm to develop a Decision Support in Heart Disease Prediction System.And this is very helpful in extracting hidden useful information from the heart disease database.. Seenivasagam.et.al [7] performed a work “Review Of Heart Disease Prediction System Using Data Mining And Hybrid Intelligent Technique” explained the classification of heart diseases through DM, one of the great advantage is to predict dieases using neural network technique and another advantage is that the data is processed already which facilitate the performance.
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Review- Influencing the heart disease using assortment of data mining tools and techniques

Review- Influencing the heart disease using assortment of data mining tools and techniques

Relying on a person’s heredity, diet possibly will or not is an imperative aspect in exhausting heart sickness. Work out is also useful for everybody in exhausting heart sickness. When taking into consideration individual expansion, together with the pessimistic possessions of heart disease, humans at a standstill have a batch to study about the human carcass and the dealings of diet, the surroundings, and heredity. Nearly everybody undergo from cardiac sickness that comprises of lots of other issues such as brain disability that additional fallout in demise of the enduring. This disease arises owing to mistaken ingestion of the fast foodstuff and sealed cooked foods which are unhealthful for the ailment prone body. In tidy to preserve the appropriate steadiness of the human body, one be supposed to have to pursue the proper diet pan in order to get rid of unusual cardiovascular diseases. To predict it carefully and properly being aware of the disease, we induce various tools being used in the prediction of the heart disease followed by the algorithms mentioned towards the accuracy levels of the prediction of disease. In this paper, an assortment of data mining tools and techniques to appropriately influence the heart disease enthusiastic to the accurateness level forecast.
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A pyramid-like model for heartbeat classification from ECG recordings

A pyramid-like model for heartbeat classification from ECG recordings

heartbeats together may cause high misclassification on S beats in particular. A number of fac- tors need to be further considered in classification: (1) ECG recordings are imbalanced and usually dominated by the N beats; (2) Some shape-related features must be included to distin- guish the V beats from the N beats for they have different QRS complexes; (3) The N and S beats are similar in QRS complex morphology, but the S beats have a fast heart rhythm. In other words, the existence of the shape-related features makes a S beat be easily misidentified as a N beat. In this study, we aim to propose a pyramid-like model to solve these problems and improve the heartbeat classification performance.
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Using Machine Learning Classification Methods to Detect the Presence of Heart Disease

Using Machine Learning Classification Methods to Detect the Presence of Heart Disease

The different results are compared based on accuracy performance, confusion matrix and area under the Receiver Operating Characteristics (ROC) curve in order to choose the best model to fix the situation and provide a predictive model for detect heart disease. Finally, it is used a hierarchical learning technique and deep learning (H2O 3.8.2.6.) to compare the previous results.[16][17][18]

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													Prediction of heart disease and strategic decision making for phi of medical dataset

1. Prediction of heart disease and strategic decision making for phi of medical dataset

Abstract- Individuals take standard medical examinations for the most part not for finding virus but rather to have genuine feelings of serenity with respect to their wellbeing status. Along these lines, it is imperative to give them a general criticism as for all the wellbeing markers that have been positioned against the entire populace. Here, we propose a framework for prediction of heart disease for a taken dataset. Especially, the highest health risk is revealed in the cases of people who are having heart disease and not predicting it before hand. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy.
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PRIVACY PRESERVING USING ENSEMBLE CLASSIFICATION FOR HEART DISEASE DATA SETS

PRIVACY PRESERVING USING ENSEMBLE CLASSIFICATION FOR HEART DISEASE DATA SETS

[7] In this study Neural network technique is adopted for classification of medical dataset. The experiment is conducted with Heart Disease dataset by considering the single and multilayer neural network modes. Back propagation algorithm with momentum and variable learning rate is used to train the networks. To analyze performance of the network various test data are given as input to the network. Parallelism is implemented at each neuron in all hidden and output layers to speed up the learning process. The experimental results proved that neural networks technique provides satisfactory results for the classification task.
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