A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Abstract

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).

Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health

“…Several machine learning classification algorithms have been used in Predictive Model Selection (PMS), namely, support vector machine (SVM), decision tree classifier (DTC), random forest (RF), logistic regression (LR), gradient boosting (GB), XGBoost, adaptive boosting (AB) and k-nearest neighbour (KNN). The authors refer to them theoretically but do not support their efficiency in their implementation [31].…”

“…There are several techniques for the diagnosis of hypertension among them SVM, Navie Bayes, which we analyzed and compared, adding the following comparatives Tables 11 and 12. [30][31][32][33].…”

“…The kernel of an SVM is an important element, which can convert low dimensional data into a higher dimensional space, ref. [31]. The authors explain in a very limited way without indicating in what way it can be implemented [31].…”

“…[31]. The authors explain in a very limited way without indicating in what way it can be implemented [31].…”

“…Cervical cancer can be diagnosed with the help of algorithms such as decision tree, logistic regression and support vector machine (SVM) [31].…”