An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers

Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.

贫血是一类以血液中血红蛋白（haemoglobin）水平低下为特征的疾病，可累及所有年龄段、所有族裔人群，是全球主要公共卫生关切之一。贫血需尽早确诊，以便及时开展治疗与干预，从而降低并发症发生率、改善患者预后。机器学习（machine learning，ML）可提升诊断精准度、加快患者诊疗流程，现已成为医疗领域的有力工具。因此，本研究探讨了机器学习方法在预测血红蛋白相关性贫血中的应用。本研究基于一组异质性血液标志物数据集，对多种机器学习算法的性能展开了研究，包括逻辑回归、CatBoost、XGBoost决策树、K近邻（KNN）等。研究进一步采用自定义堆叠集成策略对上述算法进行集成。本研究采用可解释人工智能（explainable artificial intelligence，XAI）方法对机器学习模型的决策进行解释。XGBoost模型与堆叠分类器的准确率、精确率与召回率均达到了99%。本研究证实，机器学习模型可助力贫血的早期诊断与治疗，最终改善患者预后与医疗服务质量。总体而言，本研究凸显了跨学科合作在解决复杂医学难题中的重要价值。