Performance Analysis of Supervised Classifiers Using PCA Based Techniques on Breast Cancer

Mushtaq, Zohaib; Yaqub, Akbari; Hassan, Ali; Su, Shaojian

doi:10.1109/ceet1.2019.8711868

Cited by 25 publications

(10 citation statements)

References 3 publications

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“…There are multiple parameters for the PCA kernel, linear, sigmoid, cosine, polynomial, and radial basis functions ( Mushtaq et al, 2019 ). The choice of kernel function for feature extraction purposes is based on the data itself.…”

Section: Methodsmentioning

confidence: 99%

A deep learning approach to predict blood-brain barrier permeability

Alsenan

Al-Turaiki

Hafez

2021

PeerJ Computer Science

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The blood–brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson’s, Alzheimer’s, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood–brain barrier. However, predicting compounds with “low” permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood–brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

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Section: Methodsmentioning

confidence: 99%

A deep learning approach to predict blood-brain barrier permeability

Alsenan

Al-Turaiki

Hafez

2021

PeerJ Computer Science

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“…Metrics analysis should be appropriately interpreted while assessing various learning methods. Some of the metrics generated from the confusion matrix are used to assess a diagnostic test for the classification of breast cancer [48,49] and human physiological conditions [50,51] using various ML classifiers. The confusion matrix includes a few key terms, such as A = True positive (TP), B = True negative (TN), C = False positive (FP), and D = False negative (FN).…”

Section: Performance Assessment Metricsmentioning

confidence: 99%

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

et al. 2021

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Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain.

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“…Along with other datasets, they tested their approach on Wisconsin Breast Cancer dataset and achieved 98.08% classification accuracy that outclass the existing methods. Mushtaq et al (2019) compared five different classification algorithms combined with principle-component analysis (PCA) to classify breast cancer tumor. The highest accuracy of 99.20% was obtained with sigmoid based NB classifier.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Diagnosis Using Wrapper-Based Feature Selection and Artificial Neural Network

Naveed

MADHLOOM

HUSAIN

2021

acs

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Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.

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Performance Analysis of Supervised Classifiers Using PCA Based Techniques on Breast Cancer

Cited by 25 publications

References 3 publications

A deep learning approach to predict blood-brain barrier permeability

A deep learning approach to predict blood-brain barrier permeability

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

Breast Cancer Diagnosis Using Wrapper-Based Feature Selection and Artificial Neural Network

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