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<p>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.</p>

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Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

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  • Vatsala Anand,
  • Ajay Khajuria,
  • Rupendra Kumar Pachauri,
  • Vinay Gupta

摘要

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.