Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Rahman, Akizur; Muniyandi, Ravie Chandren

doi:10.18517/ijaseit.8.4-2.6790

Cited by 24 publications

(12 citation statements)

References 34 publications

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“…The purpose of dimensionality reduction is to obtain optimal features rapidly, since the more the data dimensionality is, the more the optimization of features will get slower. So, feature selection's primary goal is to ascertain only minor features from a specific problem domain that signifies high-ranking classification performance [8,9]. or proteins form hierarchical tree structures during analysis, where every leaf node represents a specific feature.…”

Section: Feature Selectionmentioning

confidence: 99%

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

et al. 2020

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Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

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Section: Feature Selectionmentioning

confidence: 99%

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

et al. 2020

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“…In this regards, the use of machine learning techniques, which are increasingly being developed in a wide variety of areas [30][31][32][33][34] including the field of hearing [35], would allow us the extraction of additional information with respect to the mere count of occurrences from the spike patterns, leading us to an improvement of the detection performances. Hereof, since today's machine learning systems are frequently based on neural networks [36][37][38][39][40] (among which the SNNs [41]) a direction could be that of using nature-inspired recognition systems, which would allow us to better mimic the subsequent stages of PAS (i.e., SNC part of fig. 1) and to expand the system to model the complete HAS.…”

Section: Discussionmentioning

confidence: 99%

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

Susi

Bartolacci

Massarelli

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body's most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step we provided a test signal to the system, at the edge of the hearing threshold. As next step we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.

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“…In this paper, HOFS provides the most valuable 38 feature vectors which are chosen for classification. This part makes centre around acquiring a subset of F that can accomplish better execution of classification [31], [32]. In this paper, mammogram images are labeled as normal or abnormal lesion.…”

Section: Proposed Hybrid Optimum Feature Selection Methodsmentioning

confidence: 99%

An enhancement of mammogram images for breast cancer classification using artificial neural networks

Patel

Hadia

2021

IJ-AI

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Breast cancer is the most driving reason for death in women in both developed and developing nations. For the plan of effective classification of a system, the selection of features method must be used to decrease irregularity part in mammogram images. The proposed approach is used to crop the region of interests (ROIs) manually. Based on that number of features are extracted. In this proposed method a novel hybrid optimum feature selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this classification. A number of selected features is applied to train the neural network. In this proposed method accessible informational index from the mini–mammographic image analysis society (MIAS) database was used. The classification of this mammogram database involved a neural networks classifier which attained an accuracy of 99.7% with a sensitivity of 99.5%, and specificity of 100% as the area under the curve (AUC) is 0.9975 and matthew’s correlation coefficient (MCC) represents a binary class value which reached the value of 0.9931. It can be useful in a computer-aided diagnosis system (CAD) framework to help the radiologist in analyzing breast cancer. Results achieved with the proposed method are better compared to recent work.

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Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Cited by 24 publications

References 34 publications

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

An enhancement of mammogram images for breast cancer classification using artificial neural networks

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