Cervical cancer is one of the deadliest diseases in women worldwide. It is caused by long-term infection of the skin cells and mucosal cells of the genital area of women. The most disturbing thing about this cancer is the fact that it does not show any symptoms when it occurs. In the diagnosis and prognosis of cervical cancer disease, machine learning has the potential to help detect it at an early stage. In this paper, we analyzed different supervised machine learning techniques to detect cervical cancer at an early stage. To train the machine learning model, a cervical cancer dataset from the UCI repository was used. The different methods were evaluated using this dataset of 858 cervical cancer patients with 36 risk factors and one outcome variable. Six classification algorithms were applied in this study, including an artificial neural network, a Bayesian network, an SVM, a random tree, a logistic tree, and an XG-boost tree. All models were trained with and without a feature selection algorithm to compare the performance and accuracy of the classifiers. Three feature selection algorithms were used, namely (i) relief rank, (ii) wrapper method and (iii) LASSO regression. The maximum accuracy of 94.94% was recorded using XG Boost with complete features. It is also observed that for this dataset, in some cases, the feature selection algorithm performs better. Machine learning has been shown to have advantages over traditional statistical models when it comes to dealing with the complexity of large-scale data and uncovering prognostic features. It offers much potential for clinical use and for improving the treatment of cervical cancer. However, the limitations of prediction studies and models, such as simplified, incomplete information, overfitting, and lack of interpretability, suggest that further efforts are needed to improve the accuracy, reliability, and practicality of clinical outcome prediction.

宫颈癌是全世界女性最致命的疾病之一。它是由女性生殖器部位的皮肤细胞和黏膜细胞长期感染引起的。这种癌症最令人不安的是，它发生时没有任何症状。在宫颈癌疾病的诊断和预后中，机器学习有可能帮助早期发现它。在本文中，我们分析了用于早期检测宫颈癌的不同监督机器学习技术。为了训练机器学习模型，使用了来自 UCI 存储库的宫颈癌数据集。使用这个包含 858 名宫颈癌患者的数据集评估了不同的方法，这些患者有 36 个风险因素和一个结果变量。本研究应用了六种分类算法，包括人工神经网络、贝叶斯网络、SVM、随机树、逻辑树和 XG-boost 树。所有模型都在使用和不使用特征选择算法的情况下进行训练，以比较分类器的性能和准确性。使用了三种特征选择算法，即（i）浮雕等级，（ii）包装方法和（iii）LASSO回归。使用具有完整功能的 XG Boost 记录了 94.94% 的最大准确率。还观察到，对于这个数据集，在某些情况下，特征选择算法表现更好。在处理大规模数据的复杂性和揭示预后特征方面，机器学习已被证明比传统的统计模型具有优势。它为临床应用和改善宫颈癌的治疗提供了很大的潜力。然而，