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

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 is...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 30358 - 13
Hlavní autoři:	Anand, Vatsala, Khajuria, Ajay, Pachauri, Rupendra Kumar, Gupta, Vinay
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 19.08.2025 Nature Publishing Group Nature Portfolio
Témata:	639/166 692/699 Accuracy Algorithms Automation Blood pressure Cancer Classification Comparative analysis Datasets Decision Trees Deep learning Diseases Feature selection Health Health problems Humanities and Social Sciences Humans Kidney diseases Kidney Neoplasms - classification Kidney Neoplasms - diagnosis Kidney Neoplasms - pathology Kidney stones Kidneys Learning algorithms Literature reviews Lung cancer Machine Learning Medical research Mortality multidisciplinary Patients Prediction models Radiomics Regression analysis Research methodology Science Science (multidisciplinary) Support Vector Machine Support vector machines Transfer learning Tumors Transfer learning Health Mortality Classification Cancer Diseases
ISSN:	2045-2322, 2045-2322
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-15414-w