An Accurate Ckd Prediction Model With Using Integrated Layered Network With Optimizer
DOI:
https://doi.org/10.52783/jns.v14.3009Keywords:
CKD, deep learning, layered network, optimization, predictionAbstract
Predictive modeling course of CKD and ensuring early diagnosis is essential for individualized treatment that may enhance patients' quality of life and prolong survival time. Using readily available clinical and laboratory data from patients with mental health conditions CKD, this thesis investigates the interpretability of statistical learning and computer vision models for predicting end-stage renal disease. Machine-learning models were used with clinical, comorbid, and demographic data to ascertain whether a patient with CKD would experience end-stage renal disease. The most significant markers were discovered using the proposed Lightweight Layered Network (LLNet) with Stochastic Gradient Descent (SGD). Researchers also added sophisticated attribution techniques to improve the intelligibility of the neural network architecture. The neural network architecture had a much higher AUC-ROC of 99% than the baseline models. While the existing interpretation was inconsistent, the interpretation produced by Lightweight Layered Network (LLNet) with Stochastic Gradient Descent (SGD) with attribution techniques aligned with clinical expertise. There were negative connections between eGFR and urine creatinine with the progression of CKD, although positive relationships were seen with urine albumin to creatinine ratio, potassium, hematuria, and proteinuria. In conclusion, attribution algorithms combined with deep learning can detect comprehensible aspects of the development of CKD. Our model found several essential but underreported characteristics that could be new indicators of the advancement of CKD. This study gives doctors a strong, empirically supported basis for using predictive analytics in the healthcare management and therapy of CKD.
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