A Comparative Analysis of Nonlinear Machine Learning Algorithms for Breast Cancer Detection

Bataineh, Ali Al

doi:10.18178/ijmlc.2019.9.3.794

Cited by 64 publications

(34 citation statements)

References 12 publications

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“…When a new pattern is classified, it must be compared to others using a similarity measure taking into consideration the k -neighbors. The distance between the new pattern and the neighbor is used as the weight [ 47–49 ]. The most prevalent method to measure this distance is Euclidean.…”

Section: Methodsmentioning

confidence: 99%

A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

Naeem

Mabrouk

Marzouk

2020

Briefings in Bioinformatics

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Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

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Section: Methodsmentioning

confidence: 99%

A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

Naeem

Mabrouk

Marzouk

2020

Briefings in Bioinformatics

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“…Author used the K Fold validation method to predict the accuracy of each algorithm. The accuracy of MLP was found to be 96.70% which was higher than the KNN, CART and NB algorithm [89].…”

Section: Nonlinear and Ensemble Algorithmmentioning

confidence: 74%

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

et al. 2020

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Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This paper presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this paper provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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“…Genetic algorithms, swarm algorithms and immune system based algorithms come under this category. These algorithms do not have the backward phase and on the contrary, weights are adjusted in every iteration depending on the fitness criterion [25].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Immuno-Computing-based Neural Learning for Data Classification

Bataineh¹,

Kaur²

2019

IJACSA

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The paper proposes two new algorithms based on the artificial immune system of the human body called Clonal Selection Algorithm (CSA) and the modified version of Clonal Selection Algorithm (MCSA), and used them to train the neural network. Conventional Artificial Neural Network training algorithm such as backpropagation has the disadvantage that it can get trapped into the local optima. Consequently, the neural network is usually incapable of obtaining the best solution to the given problem. In the proposed new CSA algorithm, the initial random weights chosen for the neural networks are considered as a foreign body called an antigen. As the human body creates several antibodies in response to fight the antigen, similarly, in CSA algorithm antibodies are created to fight the antigen. Each antibody is evaluated based on its affinity and clones are generated for each antibody. The number of clones depends on the algorithm, in CSA, the number of clones is fixed and in MCSA, number of clones is directly proportional to the affinity of the antibody. Mutation is performed on clones to improve the affinity. The best antibody emerged becomes the antigen for the next round and the process is repeated for several iterations until the best antibody that satisfies the chosen criterion is found. The best antibody is problem specific. For neural network training for data classification, the best antibody represents the set of weights and biases that gives the least error. The efficiency of the algorithm was analyzed using Iris dataset. The prediction accuracy of the algorithms were compared with other natureinspired algorithms, such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and standard backpropagation. The performance of MCSA was ahead of other algorithms with an accuracy of 99.33%.

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A Comparative Analysis of Nonlinear Machine Learning Algorithms for Breast Cancer Detection

Cited by 64 publications

References 12 publications

A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

Immuno-Computing-based Neural Learning for Data Classification

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