A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

Lee

2020

IEEE Access

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Ovarian Cancer is a type of cancer that begins in ovaries posing a serious threat to women. As a result, it leads to abnormal cells which has the ability to spread to other regions of the body. A highly useful diagnostic and prognostic data for ovarian cancer research is provided by the microarray data. Typically, genes with tens of thousands of dimension are present in the microarray data of ovarian cancer. There is a systematic methodology required to analyze this data and so it is important to select the most important genes or features for the entire data to avoid the computational complexity. In this work, an integrated approach to feature selection is done by two consecutive steps. Initially, the features are selected by the standard gene selection techniques such as Correlation Coefficient, T-Statistics and Kruskal-Wallis test. The selected genes or features will be further optimized by four suitable stochastic optimization algorithms chosen here such as Central Force Optimization (CFO), Lightning Attachment Procedure Optimization (LAPO), Genetic Bee Colony Optimization (GBCO) and Artificial Algae Optimization (AAO). Finally, it is classified with five different classifiers to analyze the ovarian cancer classification and the best results are projected when Kruskal Wallis test with GBCO is conducted and classified with Support Vector Machine-Radial Basis Function (SVM-RBF) Kernel technique giving a high classification accuracy of 99.48%. Similar results are also obtained when Correlation Coefficient test with AAO is conducted and classified with Logistic Regression giving a high classification accuracy of 99.48%.

Section: Resultsmentioning

confidence: 99%

An Integrated Approach for Ovarian Cancer Classification With the Application of Stochastic Optimization

Lee

2020

IEEE Access

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“…It is classified with a 10-fold cross validation method, and the performance of it is shown in tables below. The mathematical formulae for computing the Performance Index (PI), Sensitivity, Specificity, and Accuracy are mentioned in literature, and using the same, the values are computed and exhibited [ 33 ]. PC is Perfect Classification; MC is Missed Classification, and FA is False Alarm in the expressions below.…”

Section: Resultsmentioning

confidence: 99%

“…The solution of the best element from a particular set of available alternatives can be done with the help of optimization techniques [ 33 ]. Application ranging from computer science, economics, biology, mechatronics, etc.…”

Section: Optimization Techniquesmentioning

confidence: 99%

An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

BioMed Research International

Rajaguru²,

Kim

2020

Self Cite

One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.

“…Optimization techniques have been implemented in many areas to handle different practical issues [ 37 ]. In different engineering and medical applications, the optimiztaion techniques have become a vital priority because of its wonderful properties.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

Rajaguru²,

Kim

2020

Diagnostics

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The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.