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Algorithms can assess the severity of existing CKD and predict how it might progress. This helps doctors tailor treatment plans to individual patients. Gradient Boosting is one algorithm explored for this.

Chronic Kidney Disease (CKD) is a widespread health issue with a significant impact on global public health. Early and accurate diagnosis plays a pivotal role in managing CKD and preventing its progression. This research focuses on enhancing the diagnostic capabilities for CKD through the implementation of efficient machine learning-based classification algorithms Our study leverages a diverse dataset comprising clinical, demographic, and laboratory parameters of CKD patients. We employ state-of-the-art machine learning techniques to develop robust classification models capable of accurately identifying and classifying CKD stages. The algorithms utilized include but are not limited to Support Vector Machines, Random Forests, and Neural Networks. To ensure the reliability and generalizability of the models, we adopt rigorous validation techniques and cross-validation procedures. Performance metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are employed to assess the effectiveness of the developed algorithms. The research not only aims to enhance the accuracy of CKD diagnosis but also emphasizes the importance of interpretability and transparency in machine learning models for clinical applications.

• Implements advance neural network method.
• A framework based application for deployment purpose.
• High scalability.
• Less time complexity.
• A specific scope of Chatbot response model, using json data format.

• Operating System: Requires a 64-bit OS (Windows 10 or Ubuntu) for compatibility with deep learning frameworks.
• Development Environment: Python 3.6 or later is necessary for coding the sign language detection system.
• Deep Learning Frameworks: TensorFlow for model training, MediaPipe for hand gesture recognition.
• Image Processing Libraries: OpenCV is essential for efficient image processing and real-time hand gesture recognition.
• Version Control: Implementation of Git for collaborative development and effective code management.
• IDE: Use of VSCode as the Integrated Development Environment for coding, debugging, and version control integration.
• Additional Dependencies: Includes scikit-learn, TensorFlow (versions 2.4.1), TensorFlow GPU, OpenCV, and Mediapipe for deep learning tasks.

Detection Accuracy: 96.7% Note: These metrics can be customized based on your actual performance evaluations.

The utilization of efficient machine learning-based classification algorithms presents a promising avenue for enhancing chronic kidney disease (CKD) detection. Through rigorous experimentation and comparative analysis, we have identified the best-performing model for this task. This advancement holds significant implications for early diagnosis and intervention, potentially improving patient outcomes and reducing healthcare burdens associated with CKD.

1.Chronic Kidney Disease Detection Using Machine Learning Technique, Rama AlMomani;Ghada Al-Mustafa;Razan Zeidan;Hiam Alquran;Wan Azani Mustafa;Ahmed Alkhayyat; 2022 5th International Conference on Engineering Technology and its Applications (IICETA)

2.Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease, Nishin James; Jitendra Kaushik; 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

3.Chronic Kidney Disease Detection using Machine Learning Algorithms: A Sri Lankan Study, D. V. Dissanayake;S. Sobana;B. Yogarajah;R. Nagulan;

4.Chronic Kidney Disease Detection using AdaBoosting Ensemble Method and KFold Cross Validation, N. Mohana Suganthi; Jemin V.M; P. Rama;E. Chandralekha, 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)

5.Chronic Kidney Disease Prediction Using Machine Learning Algorithms and the Important Attributes for the Detection; Garima Shukla;Gaurav Dhuriya;Sofia K Pillai;Aradhna Saini; 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)