scholarly journals An improved feature selection approach for chronic heart disease detection

2021 ◽  
Vol 10 (6) ◽  
pp. 3501-3506
Author(s):  
S. J. Sushma ◽  
Tsehay Admassu Assegie ◽  
D. C. Vinutha ◽  
S. Padmashree
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Binary Classification ◽  
Classification Model ◽  
Computational Time ◽  
Disease Detection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Detection Model ◽  
Sequential Feature Selection

Irrelevant feature in heart disease dataset affects the performance of binary classification model. Consequently, eliminating irrelevant and redundant feature (s) from training set with feature selection algorithm significantly improves the performance of classification model on heart disease detection. Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal features. Furthermore, exhaustive and permutation based feature selection algorithm are implemented and compared with SFS algorithm. The implemented and existing feature selection algorithms are evaluated using real world Pima Indian heart disease dataset and result appears to prove that the SFS algorithm outperforms as compared to exhaustive and permutation based feature selection algorithm. Overall, the result looks promising and more effective heart disease detection model is developed with accuracy of 99.3%.

scholarly journals Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Wen-Pei Chen ◽  
Shih-Hao Chang ◽  
Chuan-Yi Tang ◽  
Ming-Li Liou ◽  
Suh-Jen Jane Tsai ◽  
...  
Keyword(s):  
Feature Selection ◽  
Periodontal Disease ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Periodontal Diseases ◽  
Computational Time ◽  
Composition Analysis ◽  
Oral Microbiota ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

Periodontitis is an inflammatory disease involving complex interactions between oral microorganisms and the host immune response. Understanding the structure of the microbiota community associated with periodontitis is essential for improving classifications and diagnoses of various types of periodontal diseases and will facilitate clinical decision-making. In this study, we used a 16S rRNA metagenomics approach to investigate and compare the compositions of the microbiota communities from 76 subgingival plagues samples, including 26 from healthy individuals and 50 from patients with periodontitis. Furthermore, we propose a novel feature selection algorithm for selecting features with more information from many variables with a combination of these features and machine learning methods were used to construct prediction models for predicting the health status of patients with periodontal disease. We identified a total of 12 phyla, 124 genera, and 355 species and observed differences between health- and periodontitis-associated bacterial communities at all phylogenetic levels. We discovered that the generaPorphyromonas,Treponema,Tannerella,Filifactor, andAggregatibacterwere more abundant in patients with periodontal disease, whereasStreptococcus,Haemophilus,Capnocytophaga,Gemella,Campylobacter, andGranulicatellawere found at higher levels in healthy controls. Using our feature selection algorithm, random forests performed better in terms of predictive power than other methods and consumed the least amount of computational time.

scholarly journals Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Real Life ◽  
Classification Model ◽  
Cluster Method ◽  
Feature Interaction ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Multilabel Classification ◽  
Overlapping Clustering

Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC. Typically, existing cluster-based MLC frameworks employ a hard cluster method. In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class. Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm. Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable.

Feature Selection Algorithm Using Relative Odds for Data Mining Classification

2020 ◽  
pp. 81-106 ◽  
Author(s):  
Donald Douglas Atsa'am
Keyword(s):  
Feature Selection ◽  
Binary Classification ◽  
Initial Step ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Classification Problems ◽  
Odds Ratios ◽  
Relative Odds ◽  
Importance Ranking ◽  
Selection Algorithms

A filter feature selection algorithm is developed and its performance tested. In the initial step, the algorithm dichotomizes the dataset then separately computes the association between each predictor and the class variable using relative odds (odds ratios). The value of the odds ratios becomes the importance ranking of the corresponding explanatory variable in determining the output. Logistic regression classification is deployed to test the performance of the new algorithm in comparison with three existing feature selection algorithms: the Fisher index, Pearson's correlation, and the varImp function. A number of experimental datasets are employed, and in most cases, the subsets selected by the new algorithm produced models with higher classification accuracy than the subsets suggested by the existing feature selection algorithms. Therefore, the proposed algorithm is a reliable alternative in filter feature selection for binary classification problems.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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