基于SpringBoot+MySQL+SSM+Vue.js的智慧医疗管理系统(附论文),用户,管理员角色等(具体看下演示视频提供)。 如今社会上各行各业,都在用属于自己专用的软件来进行工作,互联网发展到这个时候,人们已经发现离不开了互联网。互联网的发展,离不开一些新的技术,而新技术的产生往往是为了解决现有问题而产生的。针对于医疗信息管理方面的不规范,容错率低,管理人员处理数据费工费时,采用新开发的長安智慧医疗管理系统可以从根源上规范整个数据处理流程的正规性和合法性。 長安智慧医疗管理系统实现的功能包括健康资讯管理,药品管理,病例管理,药品订单管理,医生预约管理,药方管理,客服聊天管理等功能。该系统采用了Mysql数据库,Java语言,SSM框架等技术进行编程实现。
Smart medical management system based on SpringBoot+MySQL+SSM+Vue.js (with paper attached), users, administrator roles, etc. (see the demo video for details). Nowadays, all walks of life in the society are using their own special software to work. At this time, people have found that the Internet is indispensable. The development of the Internet is inseparable from some new technologies, and new technologies are often produced to solve existing problems. In view of the nonstandard medical information management, low fault tolerance rate, and time-consuming data processing by managers, the newly developed Chang an Intelligent Medical Management System can fundamentally standardize the regularity and legitimacy of the entire data processing process. The functions of Chang an smart medical management system include health information management, drug management, case management, drug order management, doctor appointment management, prescription management, customer service chat management (2024-05-16, Java, 0KB, 下载0次)
一个机器学习项目专注于使用支持向量机（SVM）预测乳腺癌。该项目利用威斯康星州乳腺癌（诊断）数据集来训练和评估SVM分类器。
A machine learning project focused on predicting breast cancer using Support Vector Machine (SVM). The project utilizes the Breast Cancer Wisconsin (Diagnostic) dataset to train and evaluate an SVM classifier. (2024-02-15, Jupyter Notebook, 0KB, 下载0次)
使用KNN、朴素贝叶斯和决策树预测乳腺癌预期寿命。分析国家癌症研究所数据集以进行准确预测。
Predict breast cancer life expectancies with KNN, Naive Bayes, and Decision Trees. Analyzing the National Cancer Institute dataset for accurate predictions. (2023-12-27, Jupyter Notebook, 0KB, 下载0次)
#项目概述#目标：基于提供的特征开发一个诊断乳腺癌的预测模型#数据来源：威斯康星州乳腺癌（诊断）数据集#主要特征：包括细胞核的半径、纹理、周长、面积、平滑度等测量。
#Project Overview #Objective: Develop a predictive model for diagnosing breast cancer based on provided features. #Data Source: Breast Cancer Wisconsin (Diagnostic) Data Set. #Key Features: Includes measurements like radius, texture, perimeter, area, smoothness, etc., of the cell nuclei. (2023-12-27, Jupyter Notebook, 0KB, 下载0次)
本项目的目标是利用威斯康星州乳腺癌（诊断）数据的数据分析和可视化方面...
The objective of this project is to use the aspects of data analysis and visualization on the Breast Cancer Wisconsin (Diagnostic) Data Set. This project aimes to observe the relation between various features and potentially to observe which features are most most helpful in predicting malignant or benign cancer in the future. (2023-11-26, Jupyter Notebook, 0KB, 下载0次)
使用streamlit创建一个应用程序，使用（威斯康星乳腺癌（诊断）数据集）帮助预测乳腺癌，
creat an app with streamlit that helps predicting breast cancer using (Breast Cancer Wisconsin (Diagnostic) Data Set), (2023-10-25, Jupyter Notebook, 0KB, 下载0次)
在SAS中使用logistic回归预测威斯康星大学数据集的乳腺癌，模型精度为98%，
Predicted breast cancer from the University of Wisconsin dataset using logistic regression with a model accuracy of 98% in SAS, (2023-09-27, SAS, 0KB, 下载0次)
根据威斯康星州乳腺癌数据集预测诊断出的乳腺癌病例为恶性或良性的概率，
Predicting the probability that a diagnosed breast cancer case is malignant or benign based on Breast cancer Wisconsin dataset, (2023-09-22, Jupyter Notebook, 0KB, 下载0次)
我们将尝试使用南斯拉夫卢布尔雅那肿瘤研究所大学医学中心的数据来预测癌症复发...，
we will try to predict cancer recurrence using data from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia.That data have been download on kaggle (2023-08-29, Jupyter Notebook, 0KB, 下载0次)
该项目包括威斯康星州乳腺癌（诊断）数据集的分析和使用，以预测...
This project consists of the analysis and use of Breast Cancer Wisconsin (Diagnostic) Data Set to predict the type of tumour in patients. (2020-07-16, matlab, 70KB, 下载0次)
在威斯康星州乳腺癌数据集上，使用Logistic回归分类器和Newton方法对患者进行分类...
I used Logistic Regression classifier with Newtons method on Wisconsin breast cancer data set to classify the patients as malignant and benign and predict with the model. (2022-01-01, Python, 121KB, 下载0次)
我在这个项目中使用的数据集是威斯康星州乳腺癌诊断数据库。数据集包括变量...
The dataset I have used for this project is the Breast Cancer Wisconsin Diagnostic Database. The dataset includes various information about breast cancer tumors, as well as classification labels of malignant or benign. The dataset has 569 instances, or data, on 569 tumors and includes information on 30 attributes, or features, such as the (2018-02-28, Jupyter Notebook, 5KB, 下载0次)
我看了一个来自威斯康星州乳腺癌数据集的流行数据集，从加州大学欧文机器学习研究所下载...
I looked at a popular dataset from the Breast Cancer Wisconsin Dataset, downloaded from UC Irvine Machine Learning Repository where my goal was to develop a classification model that predicts whether a biopsied breast cell is benign (not harmful) or malignant (cancerous), given a set of attributes (features) about cells (observations). (2017-07-07, Jupyter Notebook, 272KB, 下载0次)
应用机器学习构建一个程序，该程序可以提取和分析威斯康星医院提供的病历，以
Applied Machine Learning to build a program that can take and analyze medical records provided by Wisconsin hospital to (2017-09-05, Jupyter Notebook, 9KB, 下载0次)
威斯康星乳腺癌（诊断）数据集GitHub存储库包含机器学习模式的代码和数据...
The Breast Cancer Wisconsin (Diagnostic) Dataset GitHub repository contains code and data for a machine learning model that predicts whether a breast mass is malignant or benign based on several features extracted from digitized images of a fine needle aspirate (FNA) of a breast mass. (2023-04-07, Jupyter Notebook, 1144KB, 下载0次)
使用UCI报告上托管的威斯康星（诊断）数据集将乳腺肿瘤分类为恶性或良性...
Classification of breast tumours as malignant or benign using the Wisconsin (Diagnostic) Data Set hosted on UCI repository (2018-01-04, Python, 49KB, 下载0次)
使用威斯康星州乳腺癌（诊断）数据库，我们可以创建一个分类器，帮助诊断患者和
Using the Breast Cancer Wisconsin (Diagnostic) Database, we can create a classifier that can help diagnose patients and (2019-07-31, Jupyter Notebook, 117KB, 下载0次)
使用威斯康星大学的数据创建机器学习模型，以预测恶性或良性乳腺癌的趋势。
Create Machine Learning Model using data from Wisconsin University to predict malignant or benign breast cancer trends. (2022-10-19, Jupyter Notebook, 8895KB, 下载0次)
机器学习技术可以用来克服这些缺点，这些缺点是由于高维的学习模型所导致的...
Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer. (2020-09-14, Jupyter Notebook, 717KB, 下载2次)
艾斯医药系统是基于互联网的应用软件,通过它能了解到已公开发布的商品，对自己需要的商品进行采购。包括查询商品，购买商品，下订单等流程，方便快捷实现购物过程。
Ice medical system is Internet-based applications, it has learned through the publicly available goods, the goods they need to purchase. Including inquiries goods, buy goods, orders and other processes, to achieve quick and easy shopping. (2011-08-06, Windows_Unix, 764KB, 下载8次)