Group of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran.
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116, Iran.
J Med Syst. 2019 Apr 26;43(6):158. doi: 10.1007/s10916-019-1259-8.
Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best method to treat asthma is to control it. Physicians recommend continuous monitoring on asthma symptoms and offering treatment preventive plans based on the patient's control level. Therefore, successful detection of the disease control level plays a critical role in presenting treatment plans. In view of this objective, we collected the data of 96 asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. A new ensemble learning algorithm with combining physicians' knowledge in the form of a rule-based classifier and supervised learning algorithms is proposed to detect asthma control level in a multivariate dataset with multiclass response variable. The model outcome resulting from the balancing operations and feature selection on data yielded the accuracy of 91.66%. Our proposed model combines medical knowledge with machine learning algorithms to classify asthma control level more accurately. This model can be applied in electronic self-care systems to support the real-time decision and personalized warnings on possible deterioration of asthma control level. Such tools can centralize asthma treatment from the current reactive care models into a preventive approach in which the physician's therapeutic actions would be based on control level.
全世界约有 3 亿人患有哮喘，估计有 25 万人死亡，这表明了这种疾病的严重性。如果不加以治疗，它可能会变成一个严重的公共卫生问题。治疗哮喘的最佳方法是控制它。医生建议对哮喘症状进行持续监测，并根据患者的控制水平提供治疗预防计划。因此，成功检测疾病控制水平对于提出治疗方案至关重要。针对这一目标，我们从德黑兰的一家肺病专科医院收集了 96 名哮喘患者的 9 个月数据。提出了一种新的集成学习算法，该算法将基于规则的分类器和监督学习算法的医生知识结合在一起，用于检测多变量数据集和多类别响应变量中的哮喘控制水平。对数据进行平衡操作和特征选择后的模型结果产生了 91.66%的准确率。我们提出的模型将医学知识与机器学习算法相结合，以更准确地分类哮喘控制水平。该模型可应用于电子自我保健系统，以支持实时决策和对哮喘控制水平可能恶化的个性化警告。这种工具可以将哮喘治疗从当前的被动治疗模式转变为预防模式，医生的治疗行动将基于控制水平。
