利用机器学习算法预测疾病

F. Deeba, S. Patil
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引用次数: 5

摘要

对于任何与健康有关的问题或疾病的分析,及时的调查和准确的预测起着至关重要的作用。机器学习技术已成为医疗保健领域中最重要的应用领域。医疗保健专业人员期待着最先进和可靠的医疗保健系统,以帮助在规定的时间内以极高的准确性为患者提供最佳的预后和治疗。目前正在开发各种数据挖掘技术,以便从收集的卫生数据集中提取有用的信息。使用机器学习算法开发预测系统背后的主要座右铭是在诊断过程中找到与健康相关的问题的最佳解决方案。用于实现的样本数据集包含约4922例患者的记录。根据132种症状预测42种常见病进行预后。本文还讨论了使用ML算法预测常见疾病所需的系统设计,其中医生/临床医生只需要输入患者所患的症状。到目前为止,在医疗保健领域实现的最好的ML算法是决策树,随机森林分类器和朴素贝叶斯分类器。本文给出了各种算法之间的比较结果。该系统模型对疾病的预测准确率高达95.12%。
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Utilization of Machine Learning Algorithms for Prediction of Diseases
For analysis of any health related problems or diseases, on time investigation and accuracy of prediction plays a vital role. Machine learning technology has become the most significant field that has various applications in the healthcare field. The healthcare professionals are looking forward for most advanced and reliable healthcare systems that assist in providing best possible prognosis and treatment to patients with great accuracy in stipulated amount of time. There are various data mining techniques which are being developed so as to extract useful information from the health dataset collected. The main motto behind the development of prediction system using machine learning algorithms is to find the best possible solution for the health related issues during diagnosis. The sample dataset utilized for the implementation consist a record of about 4922 patients'. The prognosis was carried out based on 132 symptoms for prediction of 42 commonly occurring diseases. The paper also discusses the system design required for prediction of common diseases using ML Algorithms where the doctors/clinicians just need to enter the symptoms with which patient is suffering. The best ML Algorithms found so far for implementation in the field of healthcare are Decision Tree, Random Forest Classifier and Naive Bayes Classifier. The paper presents the comparison results between various algorithms. The system model yields a high accuracy rate of 95.12% for prediction of diseases.
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