Islam Md Mohaimenul, Wu Chieh-Chen, Poly Tahmina Nasrin, Yang Hsuan-Chia, Li Yu-Chuan Jack
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Stud Health Technol Inform. 2018;247:166-170.
: Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct a prediction model based on machine learning algorithms. A dataset was developed with ten attributes that included 994 liver patients in which 533 patients were females and others were male. Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (RF) data mining technique with 10-fold cross-validation was used in the proposed model for the prediction of fatty liver disease. The performances were evaluated with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. In this proposed model, logistic regression technique provides a better result (Accuracy 76.30%, sensitivity 74.10%, and specificity 64.90%) among all other techniques. This study demonstrates that machine learning models particularly logistic regression model provides a higher accurate prediction for fatty liver diseases based on medical data from electronic medical. This model can be used as a valuable tool for clinical decision making.
脂肪肝疾病（FLD）被认为是全球最普遍的慢性肝病形式。脂肪肝疾病的预测是有效治疗及减少严重健康后果的一个重要因素。因此，我们基于机器学习算法构建了一个预测模型。开发了一个包含十个属性的数据集，其中有994名肝病患者，其中533名患者为女性，其余为男性。在所提出的用于预测脂肪肝疾病的模型中，使用了随机森林（RF）、支持向量机（SVM）、人工神经网络（ANN）以及采用10折交叉验证的逻辑回归（RF）数据挖掘技术。通过准确率、灵敏度、特异性、阳性预测值和阴性预测值来评估性能。在这个所提出的模型中，逻辑回归技术在所有其他技术中提供了更好的结果（准确率76.30%，灵敏度74.10%，特异性64.90%）。本研究表明，机器学习模型尤其是逻辑回归模型基于电子病历中的医学数据对脂肪肝疾病提供了更高准确率的预测。该模型可作为临床决策的一个有价值的工具。
