School of Creative Technologies, University of Bolton, Bolton, United Kingdom.
Department of Electrical Engineering, University of Sharjah, Sharjah, UAE.
PLoS One. 2022 Jul 6;17(7):e0269685. doi: 10.1371/journal.pone.0269685. eCollection 2022.
Anemia is one of the most pressing public health issues in the world with iron deficiency a major public health issue worldwide. The highest prevalence of anemia is in developing countries. The complete blood count is a blood test used to diagnose the prevalence of anemia. While earlier studies have framed the problem of diagnosis as a binary classification problem, this paper frames it as a multi class (three classes) classification problem with mild, moderate and severe classes. The three classes for the anemia classification (mild, moderate, severe) are so chosen as the world health organization (WHO) guidelines formalize this categorization based on the Haemoglobin (HGB) values of the chosen sample of patients in the Complete Blood Count (CBC) patient data set. Complete blood count test data was collected in an outpatient clinical setting in India. We used Feature selection with Majority voting to identify the key attributes in the input patient data set. In addition, since the original data set was imbalanced we used Synthetic Minority Oversampling Technique (SMOTE) to balance the data set. Four data sets including the original data set were used to perform the data experiments. Six standard machine learning algorithms were utilised to test our four data sets, performing multi class classification. Benchmarking these algorithms was performed and tabulated using both10 fold cross validation and hold out methods. The experimental results indicated that multilayer perceptron network was predominantly giving good recall values across mild and moderate class which are early and middle stages of the disease. With a good prediction model at early stages, medical intervention can provide preventive measure from further deterioration into severe stage or recommend the use of supplements to overcome this problem.
贫血是全球最紧迫的公共卫生问题之一，缺铁是全球主要的公共卫生问题。贫血的高发地区是发展中国家。全血细胞计数是一种用于诊断贫血患病率的血液测试。虽然早期的研究将诊断问题框定为二元分类问题，但本文将其框定为一个多类（三类）分类问题，包括轻度、中度和重度。将贫血分类的三个类别（轻度、中度、重度）选择为世界卫生组织（WHO）根据全血细胞计数（CBC）患者数据集中所选患者的血红蛋白（HGB）值正式确定的这种分类。全血细胞计数测试数据是在印度的门诊临床环境中收集的。我们使用多数投票的特征选择来识别输入患者数据集中的关键属性。此外，由于原始数据集不平衡，我们使用合成少数过采样技术（SMOTE）来平衡数据集。使用包括原始数据集在内的四个数据集来执行数据实验。使用六种标准机器学习算法对我们的四个数据集进行多类分类测试。使用 10 折交叉验证和保留方法对这些算法进行了基准测试和制表。实验结果表明，多层感知机网络主要在轻度和中度类别中给出了良好的召回值，这两个类别是疾病的早期和中期阶段。在早期阶段有一个良好的预测模型，可以提供预防措施，防止病情进一步恶化到严重阶段，或者建议使用补充剂来解决这个问题。
