BACKGROUND Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. METHODS In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k-Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. RESULTS Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. CONCLUSION Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

中文翻译：

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实施机器学习算法以创建糖尿病患者的再次入院档案。

背景技术机器学习是人工智能的一个分支，与算法的设计和开发有关，它使当今的计算机具有学习的特性。机器学习正在逐渐发展，并已成为许多领域（例如健康，教育和商业）中的关键方法。方法在本文中，我们将机器学习应用于糖尿病数据集，目的是识别表征或解释糖尿病患者再入院特征的模式和因素组合。使用的分类器包括线性判别分析，随机森林，k最近邻，朴素贝叶斯，J48和支持向量机。结果在100,000例病例中，有78,363例是糖尿病患者，超过47％的患者再次入院。糖尿病患者更容易被再次录取，是女性，高加索人或门诊患者，或者经历了较不严格的实验室程序，治疗程序或接受了较少药物治疗的患者，因此尽管没有适当的改善或给予胰岛素治疗也可以出院已检测出HbA1c阳性。结论没有进行严格的实验室评估，诊断和药物治疗的糖尿病患者，如果出院时未经改善且未接受胰岛素治疗，则更有可能重新入院，尤其是女性，高加索人或两者兼而有之。尽管经测试HbA1c呈阳性，但仍未进行适当的改善或给予胰岛素就可以出院。结论没有进行严格的实验室评估，诊断和药物治疗的糖尿病患者，如果出院时未经改善且未接受胰岛素治疗，则更有可能重新入院，尤其是女性，高加索人或两者兼而有之。尽管经测试HbA1c呈阳性，但仍未进行适当的改善或给予胰岛素就可以出院。结论没有进行严格的实验室评估，诊断和药物治疗的糖尿病患者，如果出院时未经改善且未接受胰岛素治疗，则更有可能重新入院，尤其是女性，高加索人或两者兼而有之。