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 Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

2022 ◽  
Vol 16 ◽  
pp. 38-45
Author(s):  
O. , Bhaskaru ◽  
M. Sreedevi
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Optimization Algorithm ◽  
True Positive Rate ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Feature Based ◽  
Heart Disease Diagnosis

At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures

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 PSO-RBFNN and Proposed Algorithms of DDDWT for the Heart Disease Classification

2021 ◽  
Vol 39 (4A) ◽  
pp. 520-527
Author(s):  
Ansam S. Jabbar
Keyword(s):  
Heart Disease ◽  
High Efficiency ◽  
Pso Algorithm ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Heart Disease Diagnosis ◽  
Digital Algorithm

This paper introduced a Particle Swarm Optimization-Radial Basis Function Neural Networks (PSO-RBFNN)-based system for heart disease detection that used the PSO algorithm to optimize RBFNN parameters. The newly developed signal digital algorithm presents the results of a new image contrast enhancement approach using Double Density Discrete Wavelet transform DDDWT for extraction of features, using adaptive DDDWT for the elimination of noise, and the use of PSO and ANN methods to classify the output from the Electrocardiogram (EGGS). It also provides identification of all techniques and MATLAB codes used to improve the processes. This approach merged the global search power of the PSO algorithm with the high efficiency of RBFNN's local optimums, overcome the inconsistency of the PSO algorithm and the RBFNN downside, quickly leading to a local minimum. The results show that, as compared to other approaches, the PSO-RBFNN model of heart disease diagnosis is highly accurate in detecting and predicting.

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 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.

scholarly journals Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ashir Javeed ◽  
Sanam Shahla Rizvi ◽  
Shijie Zhou ◽  
Rabia Riaz ◽  
Shafqat Ullah Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Heart Disease ◽  
Prediction Accuracy ◽  
Feature Selection Method ◽  
Disease Diagnosis ◽  
Selection Method ◽  
Diagnostic Systems ◽  
Operating Characteristics ◽  
Heart Disease Diagnosis

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

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