一种基于推荐系统的疾病预测向患者推荐医生和医院的方法

V. Shashidhar, Pradyumna A Kubear, Matthew Antony Manoj, J. Jalapreetha, Malashree
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摘要

患者成就感已成为关注疗养院健康维护和服务的重要衡量标准。这种形状已经发展成为一种新的特征:患者一侧的视角。当前,医学数据库存储的数据增长迅速。分析这些数据对医疗决策很重要。人们普遍认为,医疗数据分析通过改善患者的工作来促进良好的护理。这种形状已经发展成为一种新的特征:患者一侧的视角。目前,以医疗数据库形式存储的数据正在迅速增长。数据分析对医疗决策具有重要意义。人们普遍认为,医疗数据分析可以通过改善患者的方向来促进良好的护理。患者长度是监测疗养院资源利用和疗养院展示最常用的结果量化。它有助于恰当地管理小猫和声明。受害者反馈考虑到阐述的意见和信念的病人和专门知识的部门。该公司可以通过多种方式收集猜测,包括凝视、审计、评论和投诉。包容,可信的支持可以系统地提出使用各种一起,包括焦点。利用后者的先锋队,我们正在创造预测评估庸医表现的实践,并记录疗养院服务和患者治理的经验。使用随机森林逐步检查数据来解决问题,并使用k - nearest - neighbors逐步检查问题,其中它使用指定查询来处理问题,以仔细检查并找到两个或多个经典约束变量和非约束变量之间的答案。他们会做调查,计算从病人那里恢复的解决方案,然后根据医院的服务或管理转换成百分比
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AN APPROACH FOR PREDICTION OF DISEASES TO SUGGEST DOCTORS AND HOSPITALS TO PATIENT BASED ON RECOMMENDATION SYSTEM
Sufferer fulfilment has become an important measurement for keeping an eye on health maintenance and gig of convalescent homes. This shape has thrived into a new feature: the perspective of the sufferer’s side of egis. Currently data stored in medical Database is growing rapidly. Analysing the data is important for medical decision making. It is extensively recognized that medical data analysis promotes well care by improving sufferer gig. This shape has thrived into a new feature: the perspective of the sufferer’s side of egis. Currently, data is stored in the form of medical Database is growing rapidly. Analysing the datum is important for medical decision making. It is extensively recognized that medical data analysis promotes well care by improving sufferer direction gig. Sufferer length is the most commonly used outcome quantify for monitoring convalescent homes resource utilization and convalescent home show. It helps to manage the kitty and pronouncement fittingly. Victim feedback takes into exposition the opinions and beliefs of patients and ministries of expertise int them. The company can collect speculations in a number of ways, including gazing at, audits and comments and complains. Inclusion, credible backing for can be systematically posed using a variety of together with, including focus lots. With latter vanguard we are creating praxis for predicting evaluate quacks performance and record the experience of the convalescent home’s services and governance the sufferers. The data is scrutinised using random forest step by step procedure to solve a problem and K-Nearest-Neighbours step by step procedure to problem where it approaches the issue with a specified query to scrutinize and find the answer between two or more canon constrained variables and non-constrained variables. They will do the survey and compute the solution revived from the patients and they convert into percentage based on hospital services or managements
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