scholarly journals Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis

2020 ◽  
Vol 10 (18) ◽  
pp. 6626 ◽  
Author(s):  
Afnan M. Alhassan ◽  
Wan Mohd Nazmee Wan Zainon
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Deep Belief Network ◽  
Belief Network ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm ◽  
Heart Disease Diagnosis

Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection, and can be utilized for handling high dimensional data. Then, the selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively.

scholarly journals Heart disease prediction using SVM based neuro-fuzzy technique in the cloud computing

2018 ◽  
Vol 7 (2.20) ◽  
pp. 153
Author(s):  
Dr M. Sadish Sendil
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Disease Prediction ◽  
Specific Level ◽  
Huge Data ◽  
Neuro Fuzzy ◽  
Heart Disease Diagnosis

Cloud computing is a technique for conveying on information development benefits in resources are recovered from the Internet through online based device and applications, as opposed to a speedy association with a server. Cloud has numerous applications in the meadows of education, social networking, and medicine. But the benefit of the cloud for medical reasons is seamless, specifically an account of the huge data generated by the healthcare industry. Heart disease diagnosis determination strategy is essential and significant issue for the patient's wellbeing. Furthermore, it will help to decrease infection to a more specific level. Computer-aided decision support method performs a vital task in medical line. Data mining gives the system and innovation to change these heaps of data into effective information for decision-making. When applying data mining techniques it carries shorter time for the prediction of the disease with more exactness. The hybrid work of preprocessing, feature selection using SVM and SVM based Neuro-Fuzzy data mining strategies utilizing as a part of the determination of the heart disease is incredibly impressive. The framework is to build up a technique for arranging for heart level of the patient relies upon highlight information utilizing Neuro-Fuzzy surmising system. The experiment is done with two different analysis that is one with preprocessed data alone and applied SVM based Neuro Fuzzy Technique and the second one is accomplished with feature selection done data and applied SVM based Neuro Fuzzy Technique. The results prove that the system result of the first one gives 92% accuracy in the heart disease prediction. The second one is giving 95.11% accuracy in the heart disease prediction.  

scholarly journals Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis

2017 ◽  
Vol 7 (2) ◽  
pp. 1023 ◽  
Author(s):  
Wiharto Wiharto ◽  
Hari Kusnanto ◽  
Herianto Herianto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Multivariate Analysis ◽  
Heart Disease ◽  
System Performance ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Features Selection ◽  
Diagnosis System

<span lang="EN-US">Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system</span><span> for</span><span lang="EN-US"> diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value</span><span> for</span><span lang="EN-US"> accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%</span><span>, accuracy 86,30% </span><span lang="EN-US"> and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors,</span><span lang="EN-US">symptoms and exercise ECG. The conclusion that can be drawn</span><span> is</span><span lang="EN-US"> that the proposed diagnosis system capable of delivering performance in the </span><span>very good</span><span lang="EN-US"> category, with a number of attributes that are not a lot of checks and a relatively low cost</span><span>.</span>

scholarly journals Modelling an Effectual Glowworm Swarm Optimization Strategy for Feature Selection in Heart Disease Prediction

2020 ◽  
Vol 9 (1) ◽  
pp. 500-505
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Common Disease ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Heart Disease Diagnosis ◽  
Sensitivity Specificity

Heart disease is measured as a common disease all over the world. The ultimate target is to provide heart disease diagnosis with improved feature selection with Glow worm swarm optimization algorithm. The anticipated model comprises of optimization approach for feature selection and classifier for predicting heart disease. This system framework comprises of three stages: 1) data processing, 2) feature selection using IGWSO approach and classification with conventional machine learning classifiers. Here, C4.5 classifier is considered for performing the function. The benchmark dataset that has been attained from UCI database was cast off for performing computation. Maximal classification accuracy has been achieved based on cross validation strategy. Outcomes depicts that performance of anticipated model is superior in contrary to other models. Simulation has been done with MATLAB environment. Metrics like accuracy, sensitivity, specificity, F-measure and recall has been evaluated

scholarly journals MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Surendran Rajendran ◽  
Osamah Ibrahim Khalaf ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Selection Process ◽  
Data Classification ◽  
Deep Belief Network ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Belief Network ◽  
Big Data Classification

AbstractIn recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

An optimization-based deep belief network for the detection of phishing e-mails

2020 ◽  
Vol 54 (4) ◽  
pp. 529-549
Author(s):  
Arshey M. ◽  
Angel Viji K. S.
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Information Needs ◽  
Selection Process ◽  
Deep Belief Network ◽  
Data Set ◽  
Content Type ◽  
Belief Network ◽  
E Mail

PurposePhishing is a serious cybersecurity problem, which is widely available through multimedia, such as e-mail and Short Messaging Service (SMS) to collect the personal information of the individual. However, the rapid growth of the unsolicited and unwanted information needs to be addressed, raising the necessity of the technology to develop any effective anti-phishing methods.Design/methodology/approachThe primary intention of this research is to design and develop an approach for preventing phishing by proposing an optimization algorithm. The proposed approach involves four steps, namely preprocessing, feature extraction, feature selection and classification, for dealing with phishing e-mails. Initially, the input data set is subjected to the preprocessing, which removes stop words and stemming in the data and the preprocessed output is given to the feature extraction process. By extracting keyword frequency from the preprocessed, the important words are selected as the features. Then, the feature selection process is carried out using the Bhattacharya distance such that only the significant features that can aid the classification are selected. Using the selected features, the classification is done using the deep belief network (DBN) that is trained using the proposed fractional-earthworm optimization algorithm (EWA). The proposed fractional-EWA is designed by the integration of EWA and fractional calculus to determine the weights in the DBN optimally.FindingsThe accuracy of the methods, naive Bayes (NB), DBN, neural network (NN), EWA-DBN and fractional EWA-DBN is 0.5333, 0.5455, 0.5556, 0.5714 and 0.8571, respectively. The sensitivity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.4558, 0.5631, 0.7035, 0.7045 and 0.8182, respectively. Likewise, the specificity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.5052, 0.5631, 0.7028, 0.7040 and 0.8800, respectively. It is clear from the comparative table that the proposed method acquired the maximal accuracy, sensitivity and specificity compared with the existing methods.Originality/valueThe e-mail phishing detection is performed in this paper using the optimization-based deep learning networks. The e-mails include a number of unwanted messages that are to be detected in order to avoid the storage issues. The importance of the method is that the inclusion of the historical data in the detection process enhances the accuracy of detection.

scholarly journals New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

2019 ◽  
Vol 10 (3) ◽  
pp. 667-678 ◽  
Author(s):  
Jalil Nourmohammadi-Khiarak ◽  
Mohammad-Reza Feizi-Derakhshi ◽  
Khadijeh Behrouzi ◽  
Samaneh Mazaheri ◽  
Yashar Zamani-Harghalani ◽  
...  
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Nearest Neighbor ◽  
Imperialist Competitive Algorithm ◽  
Disease Diagnosis ◽  
K Nearest Neighbor ◽  
Feature Selection Technique ◽  
Medical Databases ◽  
Classification Evaluation ◽  
Heart Disease Diagnosis

AbstractThe number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

scholarly journals Classification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
N. Satish Chandra Reddy ◽  
Song Shue Nee ◽  
Lim Zhi Min ◽  
Chew Xin Ying
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Causes Of Death ◽  
Disease Diagnosis ◽  
Performance Model ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Heart Disease Diagnosis ◽  
The Cost

The heart disease has been one of the major causes of death worldwide. The heart disease diagnosis has been expensive nowadays, thus it is necessary to predict the risk of getting heart disease with selected features. The feature selection methods could be used as valuable techniques to reduce the cost of diagnosis by selecting the important attributes. The objectives of this study are to predict the classification model, and to know which selected features play a key role in the prediction of heart disease by using Cleveland and statlog project heart datasets. The accuracy of random forest algorithm both in classification and feature selection model has been observed to be 90–95% based on three different percentage splits. The 8 and 6 selected features seem to be the minimum feature requirements to build a better performance model. Whereby, further dropping of the 8 or 6 selected features may not lead to better performance for the prediction model.

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