Integrative Approach for Efficient Detection of Kidney Stones based on Improved Deep Neural Network Architecture

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habits like irregular eating, junk food consumption, and lack of exercise are becoming more common, leading to various health problems, including kidney issues. These factors directly affect human kidney health. To address this, we require early detection techniques that rely on text data. Text data contains detailed information about a patient's medical history, symptoms, test results, and treatment plans, giving a complete picture of kidney health and enabling timely intervention. In this research paper, we proposed a range of sophisticated models, such as Gradient Boosting Classifier, Light GBM, CatBoost, Support Vector Classifier (SVC), Random Boost, Logistic Regression, XGBoost, Deep Neural Network (DNN), and an Improved DNN. The Improved DNN demonstrated exceptional performance, with an accuracy of 90%, precision of 89%, recall of 90%, and an F1-Score of 89.5%. By combining traditional machine learning and deep neural networks, this integrative approach enables the identification of intricate patterns in datasets. The model's data-driven processes consistently update internal parameters, guaranteeing flexibility in response to evolving healthcare settings. This research represents a notable advancement in the progress of creating a more detailed and individualised ability to diagnose kidney stones, which could potentially lead to better clinical results and patient treatment.