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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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Abstract

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL-ANN approach obtained superior results of 97% accuracy, 95% sensitivity, and 90% specificity.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Chennai, Tamil Nadu, 600062, India

    Senthil Kumar Nramban Kannan

  2. Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, Tamil Nadu, 600123, India

    Joshi Aseervatham

  3. Department of Computer Science and Business Systems Engineering, JSPM’S Rajarshi Shahu College of Engineering, Savitribai Phule Pune University, Pune, 411033, India

    Kavita Moholkar

  4. Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, 600119, India

    Mithun Palanimuthu

  5. Department of Information Technology, Paavai Engineering College, Namakkal, Tamil Nadu, 637018, India

    Saranya Marappan

  6. Department Information Technology, KCG College of Technology, Chennai, Tamil Nadu, 600097, India

    Narendran Muthusamy

  7. Department of Master of Computer Application, T. J. Institute of Technology, Chennai, 600100, India

    Banu Sathar

  8. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627451, Tamil Nadu, India

    Sudhakar Sengan

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  1. Senthil Kumar Nramban Kannan
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This article is part of the topical collection “Machine Learning for Pandemic Prediction and Control” guest edited by Anand J Kulkarni, Akash Tayal, Patrick Siarry, Arun Solanki and Ali Husseinzadeh Kashan.

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Nramban Kannan, S., Aseervatham, J., Moholkar, K. et al. A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning. SN COMPUT. SCI. 5, 353 (2024). https://doi.org/10.1007/s42979-024-02665-z

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