Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia.
Sensors (Basel). 2022 May 29;22(11):4132. doi: 10.3390/s22114132.
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).
越来越多的个人和组织开始转向机器学习（ML）和深度学习（DL）来分析大量数据并生成可操作的见解。在医疗实践中，使用基于 ML 的方案预测癌症、肾衰竭和心脏病等严重疾病的早期阶段变得越来越普遍。宫颈癌是女性中最常见的疾病之一，早期诊断可能是预防这种癌症的一种可行方法。因此，本研究提出了一种使用 ML 算法预测宫颈癌的巧妙方法。研究数据集、数据预处理、预测模型选择 (PMS) 和伪代码是提出的研究技术的四个阶段。PMS 部分报告了一系列经典机器学习方法的实验，包括决策树 (DT)、逻辑回归 (LR)、支持向量机 (SVM)、K-最近邻算法 (KNN)、自适应增强、梯度提升、随机森林和 XGBoost。就宫颈癌预测而言，随机森林 (RF)、决策树 (DT)、自适应增强和梯度提升算法的分类得分最高可达 100%。相比之下，SVM 的准确率达到 99%。计算经典机器学习技术的计算复杂性以评估模型的效果。此外，本研究还对 132 名沙特阿拉伯志愿者进行了调查，以了解他们对计算机辅助宫颈癌预测的看法，重点关注人乳头瘤病毒 (HPV)。
