Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, Jharkhand, 835215, India.
Sci Rep. 2023 Aug 23;13(1):13745. doi: 10.1038/s41598-023-40906-y.
This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute's Cancer Data Access System. The dataset was first reduced using the PCLDA method, which combines Principal Component Analysis and Linear Discriminant Analysis. Two classifiers, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN), were then applied to compare their performance. The results showed that the PCLDA-SVM model achieved an impressive accuracy rate of 97.99%, with a precision of 0.92, sensitivity of 92.83%, specificity of 97.65%, and F1 score of 0.93. Additionally, it demonstrated a low error rate of 0.016 and a Matthews Correlation Coefficient (MCC) and Kappa coefficient of 0.946. On the other hand, the PCLDA-KNN model also performed well, achieving an accuracy of 97.8%, precision of 0.93, sensitivity of 93.39%, specificity of 97.86%, an F1 score of 0.92, a high MCC and Kappa coefficient of 0.98, and an error rate of 0.006. In conclusion, the PCLDA-SVM method exhibited improved efficacy in diagnosing prostate cancer compared to the PCLDA-KNN model. Both models, however, showed promising results, suggesting the potential of these classifiers in prostate cancer diagnosis.
本研究旨在评估使用国家癌症研究所癌症数据访问系统获得的筛查数据集，不同分类模型在诊断前列腺癌中的有效性。该数据集首先使用 PCLDA 方法进行了降维，该方法结合了主成分分析和线性判别分析。然后应用了两种分类器，支持向量机（SVM）和 k-最近邻（KNN），以比较它们的性能。结果表明，PCLDA-SVM 模型的准确率达到了惊人的 97.99%，精度为 0.92，灵敏度为 92.83%，特异性为 97.65%，F1 得分为 0.93。此外，它的错误率为 0.016，马修斯相关系数（MCC）和 Kappa 系数分别为 0.946。另一方面，PCLDA-KNN 模型的表现也很好，准确率为 97.8%，精度为 0.93，灵敏度为 93.39%，特异性为 97.86%，F1 得分为 0.92，MCC 和 Kappa 系数均为 0.98，错误率为 0.006。综上所述，与 PCLDA-KNN 模型相比，PCLDA-SVM 方法在诊断前列腺癌方面表现出了更好的效果。然而，这两种模型都取得了有前景的结果，表明这些分类器在前列腺癌诊断中有潜在的应用价值。
