在今天的数字世界,随着人口的增长和污染的增加,不健康的生活习惯,比如不规律的饮食,垃圾食品消费,缺乏锻炼变得越来越普遍,导致各种健康问题,包括肾脏问题.这些因素直接影响人体肾脏健康。为了解决这个问题,我们需要依赖文本数据的早期检测技术。文本数据包含有关患者病史的详细信息,症状,测试结果,和治疗计划,全面了解肾脏健康,并及时进行干预。在这篇研究论文中,我们提出了一系列复杂的模型,如梯度提升分类器,轻型GBM,CatBoost,支持向量分类器(SVC),随机升压,Logistic回归,XGBoost,深度神经网络(DNN)改进的DNN改进的DNN表现出卓越的性能,准确率为90%,精度为89%,召回90%,F1评分为89.5%。通过将传统机器学习和深度神经网络相结合,这种综合方法可以识别数据集中的复杂模式。模型的数据驱动进程持续更新内部参数,保证适应不断变化的医疗环境的灵活性。这项研究在创建更详细和个性化的诊断肾结石的能力方面取得了显着进步,这可能会导致更好的临床结果和患者治疗。
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.