A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV).

[1]  Y. Kondratenko,et al.  Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing , 2022, Sensors.

[2]  P. Shukla,et al.  A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques , 2022, Journal of healthcare engineering.

[3]  Ming Ni,et al.  Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review , 2022, Cancers.

[4]  Abdulhafis Abdulazeez Osuwa,et al.  Importance Of Continuous Improvement of Machine Learning Algorithms From A Health Care Management And Management Information Systems Perspective , 2021, 2021 International Conference on Engineering and Emerging Technologies (ICEET).

[5]  Paweł Pławiak,et al.  Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network , 2021, Sensors.

[6]  Wei Zhang,et al.  Federated learning for machinery fault diagnosis with dynamic validation and self-supervision , 2021, Knowl. Based Syst..

[7]  Sicco Verwer,et al.  Efficient Training of Robust Decision Trees Against Adversarial Examples , 2020, ICML.

[8]  A. Prakash,et al.  Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application , 2020, Journal of Business Research.

[9]  Gonzalo Martínez-Muñoz,et al.  A comparative analysis of gradient boosting algorithms , 2020, Artificial Intelligence Review.

[10]  Rebecka Weegar,et al.  Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations , 2020, PloS one.

[11]  A. F.,et al.  Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer , 2020, Journal of biomedical physics & engineering.

[12]  Jiann-Shing Shieh,et al.  Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography , 2020, Sensors.

[13]  Ahmed Ghoneim,et al.  Machine learning for assisting cervical cancer diagnosis: An ensemble approach , 2020, Future Gener. Comput. Syst..

[14]  M. Mikov,et al.  Cervical Cancer, Different Treatments and Importance of Bile Acids as Therapeutic Agents in This Disease , 2019, Front. Pharmacol..

[15]  B. Nithya,et al.  Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction , 2019, SN Applied Sciences.

[16]  Sanjay Purushotham,et al.  Survival outcome prediction in cervical cancer: Cox models vs deep‐learning model , 2019, American journal of obstetrics and gynecology.

[17]  N. Sivakumar,et al.  IoT based heart disease prediction and diagnosis model for healthcare using machine learning models , 2019, 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN).

[18]  Zhongwei Li,et al.  Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach , 2019, Cancers.

[19]  Sebastian Raschka,et al.  Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning , 2018, ArXiv.

[20]  Andino Maseleno,et al.  Optimal feature-based multi-kernel SVM approach for thyroid disease classification , 2018, The Journal of Supercomputing.

[21]  Akshitha Shetty,et al.  Survey of Cervical Cancer Prediction Using Machine Learning: A Comparative Approach , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[22]  Shuwen Han,et al.  Role of Lactobacillus in cervical cancer , 2018, Cancer management and research.

[23]  Jaime S. Cardoso,et al.  Transfer Learning with Partial Observability Applied to Cervical Cancer Screening , 2017, IbPRIA.

[24]  Alempije Veljovic,et al.  Evaluation of Classification Models in Machine Learning , 2017 .

[25]  R. Ndejjo,et al.  Women’s knowledge and attitudes towards cervical cancer prevention: a cross sectional study in Eastern Uganda , 2017, BMC Women's Health.

[26]  Santi Wulan Purnami,et al.  Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine , 2016 .

[27]  Ashley K Kable,et al.  A structured approach to documenting a search strategy for publication: a 12 step guideline for authors. , 2012, Nurse education today.

[28]  Z. Sharif-Khodaei,et al.  SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels , 2012 .

[29]  Massimiliano Pontil,et al.  Support Vector Machines: Theory and Applications , 2001, Machine Learning and Its Applications.

[30]  M. Shamim Hossain,et al.  Cervical cancer classification using convolutional neural networks and extreme learning machines , 2020, Future Gener. Comput. Syst..

[31]  Jalaluddin Khan,et al.  Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare , 2020, IEEE Access.

[32]  Prabhpreet Kaur,et al.  Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification , 2019, Informatics in Medicine Unlocked.

[33]  Jaswinder Singh,et al.  Prediction of Cervical Cancer Using Machine Learning Techniques , 2019 .

[34]  Pritika Bahad,et al.  Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics , 2019 .

[35]  Muhammad Atif,et al.  Cervical Cancer Prediction through Different Screening Methods using Data Mining , 2019, International Journal of Advanced Computer Science and Applications.

[36]  Dr.P. Aruna,et al.  Comparison of Feature selection methods for diagnosis of cervical cancer using SVM classifier , 2016 .

[37]  M. Anousouya Devi,et al.  Classification of Cervical Cancer Using Artificial Neural Networks , 2016 .

[38]  Sirapat Chiewchanwattana,et al.  A Comparative Machine Learning Algorithm to Predict the Bone Metastasis Cervical Cancer with Imbalance Data Problem , 2014, IC2IT.

[39]  O. González-Recio,et al.  The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets. , 2013, Journal of dairy science.

[40]  Sharon O'Toole,et al.  Gene expression profiling in cervical cancer: identification of novel markers for disease diagnosis and therapy. , 2009, Methods in molecular biology.

[41]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .