Ionian University, Department of Informatics, Corfu, Greece.
Adv Exp Med Biol. 2021;1338:13-19. doi: 10.1007/978-3-030-78775-2_3.
Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.
乳腺癌是美国女性中第二常见的癌症类型，而且通常以侵袭性形式出现。早期发现可以潜在地降低死亡率，因为此时不太可能形成大肿瘤。过去几十年的技术进步提供了先进的工具，这些工具利用机器学习进行早期检测。常见的技术包括使用特殊设备对肿瘤进行成像，但在大多数情况下，这种设备并不能广泛获得。为了克服这一限制，正在探索利用基于血液的生物标志物的新技术。在当前的工作中，我们利用机器学习算法开发了一个基于用户信息（年龄、体重指数、血糖和抵抗素）的决策支持系统，用于乳腺癌的早期检测。所探索的算法包括逻辑回归、朴素贝叶斯、支持向量机和梯度提升分类，所有这些算法都用于根据包括以前乳腺癌事件信息的数据集对新患者进行分类。结果表明，基于当前方法和实现的最佳算法是梯度提升分类，它表现出最高的预测分数。为了确保广泛的可访问性，我们开发了一个移动应用程序。用户可以轻松地向应用程序提供预测所需的信息，并快速获得结果。
