DSXNet: A Cutting-Edge Hybrid Model for Accurate Chronic Kidney Disease Detection and Classification

Key Points

• Accurate detection of chronic kidney disease using the hybrid model is achieved through advanced machine learning techniques.
• The model demonstrates a significant accuracy rate of 92% in identifying various stages of chronic kidney disease.
• This approach involves a comprehensive hybrid model combining multiple algorithms to enhance classification accuracy.
• Improved detection strategies could lead to early interventions for chronic kidney disease, ultimately benefiting patient health.