Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates

2020 ◽  
Vol 22 (4) ◽  
pp. 1376-1388 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Hossein Ahmadi ◽  
Azizah Abdul Manaf ◽  
Tarik A. Rashid ◽  
Sarminah Samad ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Self Organizing Map ◽  
Fuzzy Support Vector Machine ◽  
Heart Disease Diagnosis ◽  
Self Organizing

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 Scene Oriented Classification of Blurry and Noisy Images Using SVM with Fuzzy C Mean Clustering

2014 ◽  
Vol 12 (4) ◽  
pp. 3393-3402
Author(s):  
Deepak Nema
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Hierarchical Classification ◽  
Computation Time ◽  
Support Vector ◽  
Self Organizing Map ◽  
Large Dataset ◽  
Noisy Images ◽  
Self Organizing

Image classification is a challenging task in image processing especially in the case of blurry and noisy images. In this work, we present an extension of scene oriented hierarchical classification of blurry and noisy images using Support Vector Machine (SVM) and Fuzzy C-Mean. Generally, a system for scene-oriented classification of blurry and noisy images attempts to simulate major features of the human visual observation. These approaches are  based on three strategies such as Global pathway for extracting essential signature of image, local pathway for extracting local features, and then outcome of both global and local phase are combined and define feature vector and clustered using Monte Carlo approach. Afterwards, these clustered results are fed to a SOTA Algorithm (combination of self organizing map and hierarchical clustering) for final classification. But in these approaches, combination of self organizing map and hierarchical clustering has the problem in terms of accuracy and computation time of classification, especially when used large dataset for classification. To overcome this problem, we propose a combination of Support Vector Machine (SVM) and Fuzzy C-mean. Our proposed approach provides better result in terms of accuracy, especially when used with large dataset. The proposed method is computationally efficient because fuzzy c-mean clustering is faster and less time consuming as compared to hierarchical clustering.

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