Anand Vatsala, Khajuria Ajay, Pachauri Rupendra Kumar, Gupta Vinay
Department of Computer Science and Engineering, Akal University, Talwandi Sabo, Bathinda, Punjab, India.
Department of Chemistry, Akal University, Talwandi Sabo, Bathinda, Punjab, India.
Sci Rep. 2025 Aug 19;15(1):30358. doi: 10.1038/s41598-025-15414-w.
The kidney is an important organ that helps clean the blood by removing waste, extra fluids, and harmful substances. It also keeps the balance of minerals in the body and helps control blood pressure. But if the kidney gets sick, like from a tumor, it can cause big health problems. Finding kidney issues early and knowing what kind of problem it has is very important for good treatment and better results for patients. In this study, different machine learning models were used to detect and classify kidney tumors. These models included Decision Tree, XGBoost Classifier, K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The dataset splitting is done in two ways 80:20 and 75:25 and the models worked best with the 80:20 split. Among them, the top three models-SVM, KNN, and XGBoost-were tested with different batch sizes, which are 16 and 32. SVM performed best when the batch size was 32. These models were also trained using two types of optimizers, called Adam and SGD. SVM did better when using the Adam method. SVM had the highest accuracy of 98. 5%, then came KNN with 90.4%. This method will help healthcare professionals in the early diagnosis of disease.
肾脏是一个重要器官，它通过清除废物、多余液体和有害物质来帮助净化血液。它还能维持体内矿物质平衡并有助于控制血压。但是，如果肾脏生病，比如患上肿瘤，就会引发严重的健康问题。尽早发现肾脏问题并了解其类型对于患者获得良好治疗效果和更佳预后非常重要。在本研究中，使用了不同的机器学习模型来检测和分类肾脏肿瘤。这些模型包括决策树、XGBoost分类器、K近邻算法（KNN）、随机森林和支持向量机（SVM）。数据集按照80:20和75:25两种方式进行划分，模型在80:20划分方式下表现最佳。其中，排名前三的模型——支持向量机、K近邻算法和XGBoost——用16和32这两种不同的批量大小进行了测试。当批量大小为32时，支持向量机表现最佳。这些模型还使用了两种优化器进行训练，分别是Adam和随机梯度下降（SGD）。支持向量机在使用Adam方法时表现更好。支持向量机的准确率最高，为98.5%，其次是K近邻算法，准确率为90.4%。这种方法将有助于医疗保健专业人员对疾病进行早期诊断。
