Recent studies have proven that data analytics may assist in predicting events before they occur, which may impact the outcome of current situations. In the medical sector, it has been utilized for predicting the likelihood of getting a health condition such as chronic kidney disease (CKD). This paper aims at developing a CKD prediction framework, which forecasts CKD occurrence over a specific time using deep learning and deep ensemble learning approaches. While a great deal of research focuses on disease detection, few studies contribute to disease prediction before it may occur. However, the performance of previous work was not competitive. This paper tackles the under-explored area of early CKD prediction through a high-performing deep learning and ensemble framework. We bridge the gap between existing detection methods and preventive interventions by: developing and comparing deep learning models like CNN, LSTM, and LSTM-BLSTM for 6–12 month CKD prediction; addressing data imbalance, feature selection, and optimizer optimization; and building an ensemble model combining the best individual models (CNN-Adamax, LSTM-Adam, and LSTM-BLSTM-Adamax). Our framework achieves significantly higher accuracy (98% and 97% for 6 and 12 months) than previous work, paving the way for earlier diagnosis and improved patient outcomes.

中文翻译：

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基于深度学习模型和优化器集合的慢性肾脏疾病的早期预测

最近的研究证明，数据分析可以帮助在事件发生之前进行预测，这可能会影响当前情况的结果。在医疗领域，它已被用于预测患慢性肾病 (CKD) 等健康状况的可能性。本文旨在开发一个 CKD 预测框架，该框架使用深度学习和深度集成学习方法来预测特定时间内 CKD 的发生。虽然大量研究都集中在疾病检测上，但很少有研究有助于疾病发生之前的预测。然而，之前的工作表现并不具有竞争力。本文通过高性能深度学习和集成框架解决了早期 CKD 预测中尚未探索的领域。我们通过以下方式弥合现有检测方法和预防性干预措施之间的差距： 开发和比较 CNN、LSTM 和 LSTM-BLSTM 等深度学习模型，用于 6-12 个月的 CKD 预测；解决数据不平衡、特征选择和优化器优化问题；并结合最佳的单个模型（CNN-Adamax、LSTM-Adam 和 LSTM-BLSTM-Adamax）构建集成模型。与之前的工作相比，我们的框架实现了显着更高的准确率（6 个月和 12 个月分别为 98% 和 97%），为早期诊断和改善患者治疗结果铺平了道路。