The evolution of kidney risk after kidney tumor nephrectomy is an important part of postoperative management. We provide AutoInt, an automatic feature interaction predictive model intended to evaluate the risk profiles related to nephrectomy in this work. Through self-attention techniques, AutoInt integrates complicated feature interactions using a broad set of clinical data, improving clinical decision-making and patient care in this particular setting. Our findings show that AutoInt outperforms baseline machine learning systems, achieving an amazing accuracy rate of 96%. The model offers unparalleled interpretability and predictive performance due to its automatic capturing of feature interactions that include both category and numerical features. AutoInt's potential goes beyond data analysis; by providing individualized medical therapies for patients who have had nephrectomy, it could transform the area of nephrology. Healthcare professionals may make quicker and more informed judgments by expediting the diagnostic process, which will ultimately lessen the postoperative burden for these patients. AutoInt has the potential to greatly impact kidney tumor patients’ postoperative care and therapy, resulting in better outcomes and a greater standard of living. AutoInt is a ray of hope for people traversing the complicated terrain of renal risk progression after nephrectomy, with its astounding 96% accuracy.