kNN
Early detection of kidney illness can be achieved by training machine learning algorithms to discover patterns in patient data, such as imaging, test results, and medical history. This will enable rapid diagnosis and start of treatment regimens, which can improve patient outcomes. With 98.97% accuracy in CKD detection, the suggested TrioNet with KNN imputer and SMOTE fared better than other models. This comprehensive research highlights the model's potential as a useful tool in the diagnosis of chronic kidney disease (CKD) and highlights its capabilities.
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<p>Ten individuals in good health were enlisted to execute 16 distinct movements involving the wrist and fingers in real-time. Before commencing the experimental procedure, explicit consent was obtained from each participant. Participants were informed that they had the option to withdraw from the study at any point during the experimental session. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki and received approval from the local ethics committee at the National University of Sciences and Technology, Islamabad, Pakistan.
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Lantana flower, originally known as a parasitic and poisonous plant, is expansive to fill many livestock fields. Lantana data sets are open source and can be used by many researchers to create models with higher accuracy. currently the accuracy using this dataset has reached 99.8% using k-NN and preceded by feature extraction using VGG-16 Lantana flower, originally known as a parasitic and poisonous plant, is expansive to fill many livestock fields. Lantana data sets are open source and can be used by many researchers to create models with higher accuracy.
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