BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

[1]  Aditya Khamparia,et al.  Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning , 2020, The Journal of Supercomputing.

[2]  Robertas Damasevicius,et al.  COVID-19 image classification using deep features and fractional-order marine predators algorithm , 2020, Scientific Reports.

[3]  J. M. Al-Tuwaijari,et al.  Cancer Classification Using Gaussian Naive Bayes Algorithm , 2019, 2019 International Engineering Conference (IEC).

[4]  Syeda Sundus Zehra,et al.  Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks , 2020 .

[5]  Chinmay Chakraborty,et al.  Identification of Chronic Wound Status under Tele-Wound Network through Smartphone , 2015, Int. J. Rough Sets Data Anal..

[6]  Gautam Srivastava,et al.  Automated cognitive health assessment in smart homes using machine learning , 2020 .

[7]  Sang Won Yoon,et al.  Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms , 2014, Expert Syst. Appl..

[8]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[9]  Hossam Faris,et al.  Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context , 2019, J. Inf. Sci..

[10]  Celestine Iwendi,et al.  Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition , 2020, Sensors.

[11]  Tarik A. Rashid,et al.  A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm , 2019, Comput. Intell. Neurosci..

[12]  Hajar Mousannif,et al.  Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis , 2016, ANT/SEIT.

[13]  Oguz Gungor,et al.  Classification of multispectral images using Random Forest algorithm , 2012 .

[14]  FarisHossam,et al.  Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts , 2018 .

[15]  Chinmay Chakraborty,et al.  Monitoring of Epileptical Patients Using Cloud-Enabled Health-IoT System , 2019, Traitement du Signal.

[16]  Carlos Borrego,et al.  Applications in Security and Evasions in Machine Learning: A Survey , 2020 .

[17]  Abdul Rehman Javed,et al.  Collaborative Health Care Plan through Crowdsource Data using Ambient Application , 2019, 2019 22nd International Multitopic Conference (INMIC).

[18]  Ali Kashif Bashir,et al.  PARCIV: Recognizing physical activities having complex interclass variations using semantic data of smartphone , 2020, Softw. Pract. Exp..

[19]  Saif Ur Rehman,et al.  PersonalisedComfort: a personalised thermal comfort model to predict thermal sensation votes for smart building residents , 2020, Enterp. Inf. Syst..

[20]  Saurabh Pal,et al.  A Novel Approach for Breast Cancer Detection Using Data Mining Techniques , 2017 .

[21]  Rutvij H. Jhaveri,et al.  Attack‐pattern discovery based enhanced trust model for secure routing in mobile ad‐hoc networks , 2017, Int. J. Commun. Syst..

[22]  M. Mildner,et al.  Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.

[23]  Muhammet Fatih Ak,et al.  A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications , 2020, Healthcare.

[24]  Thar Baker,et al.  Analysis of Dimensionality Reduction Techniques on Big Data , 2020, IEEE Access.

[25]  Thar Baker,et al.  A collaborative healthcare framework for shared healthcare plan with ambient intelligence , 2020, Hum. centric Comput. Inf. Sci..

[26]  Rommel M. Barbosa,et al.  Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine , 2020, Medical & Biological Engineering & Computing.

[27]  Ankur Singh Bist,et al.  Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks , 2020, Neural Computing and Applications.

[28]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[29]  Mamoun Alazab,et al.  A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU , 2020, Journal of Real-Time Image Processing.

[30]  Ali Sharifi,et al.  Prediction of Breast Tumor Malignancy Using Neural Network and Whale Optimization Algorithms (WOA) , 2019, Iranian Quarterly Journal of Breast Disease.

[31]  Chandan Chakraborty,et al.  Telemedicine Supported Chronic Wound Tissue Prediction Using Classification Approaches , 2016, Journal of Medical Systems.

[32]  Sonal Jain,et al.  Analysis of k-means clustering approach on the breast cancer Wisconsin dataset , 2016, International Journal of Computer Assisted Radiology and Surgery.

[33]  Jerry Chun-Wei Lin,et al.  Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing , 2021, IEEE Access.

[34]  Praveen Kumar Reddy Maddikunta,et al.  A metaheuristic optimization approach for energy efficiency in the IoT networks , 2020, Softw. Pract. Exp..

[35]  Robertas Damasevicius,et al.  A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features , 2020, Symmetry.

[36]  Hossam Faris,et al.  Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts , 2018, Knowl. Based Syst..

[37]  Dietrich Rebholz-Schuhmann,et al.  Deep learning-based clustering approaches for bioinformatics , 2020, Briefings Bioinform..

[38]  Sapiah Binti Sakri,et al.  Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction , 2018, IEEE Access.

[39]  Waleed S. Alnumay,et al.  PP-SPA: Privacy Preserved Smartphone-Based Personal Assistant to Improve Routine Life Functioning of Cognitive Impaired Individuals , 2021, Neural Processing Letters.

[40]  Seyed Reza Kamel,et al.  Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer , 2019, Journal of Big Data.

[41]  Heyam H. Al-Baity,et al.  On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context , 2019, IEEE Access.

[42]  Jeng-Shyang Pan,et al.  Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales , 2016, ICGEC.

[43]  João Miguel da Costa Sousa,et al.  Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients , 2013, Appl. Soft Comput..

[44]  K. Sivakami Mining Big Data: Breast Cancer Prediction using DT-SVM Hybrid Model , 2015 .

[45]  T. P. Latchoumi,et al.  Abnormality detection using weighed particle swarm optimization and smooth support vector machine , 2017 .

[46]  Rutvij H. Jhaveri,et al.  The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review , 2018 .

[47]  M. B. Abdelhalim,et al.  Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers , 2012 .

[48]  Stefan Decker,et al.  A snapshot neural ensemble method for cancer-type prediction based on copy number variations , 2019, Neural Computing and Applications.

[49]  Mohamed Bahaj,et al.  Applying Best Machine Learning Algorithms for Breast Cancer Prediction and Classification , 2018, 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS).