#用户注册、登录模块

#数据库脚本

CREATE TABLE usertable(

  userid number(8) primary key not  null ,

  username varchar(50)  NULL,

  password varchar(50) NOT NULL,

  sex varchar(10) NOT NULL,

  age number(3) NOT NULL,

  province varchar2(50) null,

  area varchar(50) NOT NULL

  );

commit;

create sequence usertable_seq

minvalue 1

maxvalue 999

start with 1

increment by 1

NOCYCLE

nocache;

commit;

insert into  usertable (userid, username , password, sex,age, province ,area ) values ( usertable_seq.Nextval , '小明','','男',13,'广东省', '广州市' );

insert into  usertable (userid, username , password, sex,age, province ,area ) values ( usertable_seq.Nextval , '小红','','女',14,'广东省', '广州市' );

insert into  usertable (userid, username , password, sex,age, province ,area ) values ( usertable_seq.Nextval , '小东','','男',15,'广东省', '广州市' );

commit;

insert into  usertable (userid, username , password, sex,age, province ,area ) values ( usertable_seq.Nextval , '小东东','','男',15,'广东省', '广州市' );

commit;
#注册

import cx_Oracle

import numpy as np

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

username = input("请输入用户名：")

password = input("请输入密码：")

sex = input("请输入性别：")

age = int(input("请输入年龄："))

province = input("请输入省份：")

area = input("请输入所属区：")

sql = "insert into  usertable (userid, username , password, sex,age, province ,area ) values (usertable_seq.Nextval,'%s','%s','%s',%d,'%s','%s')" % (username, password,sex,age,province,area)

cursor.execute(sql)

conn.commit()

print("注册成功！")

#登录

import cx_Oracle

import numpy as np

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

username = input("请输入用户名：")

password = input("请输入密码：")

sql = "select userid from usertable where username='%s' and password='%s'" % (username,password)

cursor.execute(sql)

rows = cursor.fetchall()

uid = []

for row in rows:

    uid.append(row[0])

if(len(uid)==1):

    print("登录成功！")

else:

    print("登录失败！")

#诊断记录表

#数据库脚本

CREATE TABLE zhenduanjilutable(

  userid number(8) not  null ,

  username varchar(50)  NULL,

  sex varchar(10) NOT NULL,

  age number(3) NOT NULL,

  province varchar2(50) null,

  area varchar(50) NOT NULL,

  bumen varchar(26) NOT NULL,

  ke varchar(26) NOT NULL,

  result varchar(26) NOT NULL,

  chufang varchar2(2220),

  jianyi varchar2(1020),

  yiyuaan varchar2(100),

  yisheng varchar2(1100),

  jianchaxiang varchar2(1100),

  zhenduanriqi date default sysdate

  );

commit;

insert into zhenduanjilutable (userid,username,sex,age,province,area,bumen,ke,result,chufang,jianyi,yiuaan,yisheng,jianchaxiang)

values (%d,'%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s') % (,,,,,,,,,,,,,)
#智能初诊

import cx_Oracle

import numpy as np

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

uid = uid[0]

sql = "select username,sex,age,province,area from usertable where userid=%d" % uid

cursor.execute(sql)

rows = cursor.fetchall()

userinfo = []

for row in rows:

    userinfo.append(row[0])

    userinfo.append(row[1])

    userinfo.append(row[2])

    userinfo.append(row[3])

    userinfo.append(row[4])

print(userinfo)

import cx_Oracle

import numpy as np

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

sql = "select mname from hy_bumen"

cursor.execute(sql)

rows = cursor.fetchall()

bumen = []

for row in rows:

    bumen.append(row[0])

# print(bumen)

bumen = "外科"

sql = "select MENZHENID from hy_bumen where MNAME='%s'" % bumen

cursor.execute(sql)

rows = cursor.fetchall()

bumenid = []

for row in rows:

    bumenid.append(row[0])

sql = "select ke from hy_keid where MENZHENID=%d" % bumenid[0]

cursor.execute(sql)

rows = cursor.fetchall()

kelist = []

for row in rows:

    kelist.append(row[0])

# print(kelist)

ke = "胸外科"

sql = "select keid from hy_keid where ke='%s'" % ke

cursor.execute(sql)

rows = cursor.fetchall()

keid = []

for row in rows:

    keid.append(row[0])

sql = "select QUESTION from HY_QUESTID where QUID=%d" % keid[0]

cursor.execute(sql)

rows = cursor.fetchall()

question = []

for row in rows:

    question.append(row[0])

question = question[0].split(",")

# print(question)

answer = []

for i,j in zip(question,np.arange(len(question))):

    print("问题"+str(j+1)+"：是否"+i+"：")

    print("    A、正常    B、较轻    C、明显    D、非常严重")

    temp = input("请根据实际情况选择上面的一项：")

    if(temp=="A"):

        answer.append(1)

    elif(temp=="B"):

        answer.append(2)

    elif(temp=="C"):

        answer.append(3)

    else:

        answer.append(4)

# print(answer)

#使用神经网络探索数据集

import sys

import os

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

surgery = "外科"

surgeryChest = "胸外科"

#one-hot编码

def onehot(labels):

    n_sample = len(labels)

    n_class = max(labels) + 1

    onehot_labels = np.zeros((n_sample, n_class))

    onehot_labels[np.arange(n_sample), labels] = 1

    return onehot_labels

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        if(row[5]==3):

            lables.append(0)

        elif(row[5]==6):

            lables.append(1)

        else:

            lables.append(2)

    dataset = np.array(dataset)

    lables = np.array(lables)

    dataset = dataset.astype(np.float32)

    labless = onehot(lables)

    return dataset,labless

#获取测试数据集

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from test where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        if(row[5]==3):

            testlables.append(0)

        elif(row[5]==6):

            testlables.append(1)

        else:

            testlables.append(2)

    testdataset = np.array(testdataset)

    testlables = np.array(testlables)

    testdataset = testdataset.astype(np.float32)

    testlabless = onehot(testlables)

    return testdataset,testlabless

dataset,labless = getdata(surgery,surgeryChest)

# testdataset,testlables = gettestdata(surgery,surgeryChest)

# dataset = dataset[0:100]

# labless = labless[0:100]

x_data = tf.placeholder("float32", [None, 5])

y_data = tf.placeholder("float32", [None, 3])

weight = tf.Variable(tf.ones([5, 3]))

bias = tf.Variable(tf.ones([3]))

#使用softmax激活函数

y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)

#y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)

# loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

#使用交叉熵作为损失函数

loss = -tf.reduce_sum(y_data*tf.log(y_model))

# train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

#使用AdamOptimizer优化器

# train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

train_step = tf.train.MomentumOptimizer(1e-4,0.9).minimize(loss)

#评估模型

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

# start = time.time()

for _ in range(10):

    for i in range(int(len(dataset)/100)):

        sess.run(train_step, feed_dict={x_data:dataset[i:i+100,:], y_data:labless[i:i+100,:]})

# print("模型准确率",sess.run(accuracy, feed_dict={x_data:testdataset , y_data:testlables}))

# end = time.time()

# print("模型训练和测试公耗时：%.2f 秒" % (end-start))

xl_weight = sess.run(weight)

useranswer = [[1, 2, 3, 4, 4]]*3

W = np.dot(xl_weight,useranswer)

result=0

for i in range(len(W[0])):

    for j in range(len(W[0,:])):

        if(i==j):

            result += W[i][j]

result = int(result/5)

# print(result)

if(result<=3):

    result = 3

elif(result<=6):

    result = 6

else:

    result = 9

sql = "select ILL_NAME from ill_result_tbZ where FAMILY='%s' and ILL_ID=%d" % (ke,result)

cursor.execute(sql)

rows = cursor.fetchall()

ILL_NAME = []

for row in rows:

    ILL_NAME.append(row[0])

firstResult = ILL_NAME[0]

##

print("=======================系统初诊单===================")

print("姓名："+userinfo[0])

print("性别："+userinfo[1])

print("年龄："+str(userinfo[2]))

print("省份："+userinfo[3])

print("所属区："+userinfo[4])

print("初诊部门："+bumen)

print("科目："+ke)

##

print("系统智能诊断结果："+firstResult)

if(firstResult[:2]=="疑似"):

    firstResult = "疑似患病"

sql = "select PRESCRIPTION_1,PRESCRIPTION_2,PRESCRIPTION_3,PRESCRIPTION_4,PRESCRIPTION_5,PRESCRIPTION_6,PRESCRIPTION_7,PRESCRIPTION_8,PRESCRIPTION_9,PRESCRIPTION_10,PRESCRIPTION_11,PRESCRIPTION_12,PRESCRIPTION_13,PRESCRIPTION_14,PRESCRIPTION_15 from PRESCRIPTION where SURGERY='%s'and SURGERYCHEST='%s' and ILLNAME='%s'" % (bumen,ke,firstResult)

cursor.execute(sql)

rows = cursor.fetchall()

chufanggrace = []

for row in rows:

    temp = []

    temp.append(row[0])

    temp.append(row[1])

    temp.append(row[2])

    temp.append(row[3])

    temp.append(row[4])

    temp.append(row[5])

    temp.append(row[6])

    temp.append(row[7])

    temp.append(row[8])

    temp.append(row[9])

    temp.append(row[10])

    temp.append(row[11])

    temp.append(row[12])

    temp.append(row[13])

    temp.append(row[14])

    chufanggrace.append(temp)

PRESCRIPTION_sum = []

for col in range(np.shape(chufanggrace)[1]):

    temp = 0

    for row in range(np.shape(chufanggrace)[0]):

        temp += chufanggrace[row][col]

    PRESCRIPTION_sum.append(temp)

b = sorted(enumerate(PRESCRIPTION_sum),key=lambda x:x[1],reverse=True)[:3]

index = []

for i in b:

    index.append(i[0])

sql = "select PRESCRIPTIONINFO,HEALTH from PRESCRIPTIONINFO where DEPARTMENT='%s' and FAMILY='%s' and ILL_NAME='%s'" % (bumen,ke,firstResult)

cursor.execute(sql)

rows = cursor.fetchall()

chufanglist = []

jianyilist = []

for row in rows:

    chufanglist.append(row[0])

    jianyilist.append(row[1])

best_chufang = []

best_jianyi = []

for i in index:

    best_chufang.append(chufanglist[i])

    best_jianyi.append(jianyilist[i])

chufang_str = ""

jianyi_str = ""

for i,j in zip(best_chufang,range(len(best_chufang))):

    chufang_str += "系统智能筛选优良处方"+str(j+1)+"：" + i +"。"

    print("系统智能筛选优良处方"+str(j+1)+"：" + i)

for i,j in zip(best_jianyi,range(len(best_jianyi))):

    jianyi_str += "系统智能筛选优良养生建议"+str(j+1)+"：" + i+"。"

    print("系统智能筛选优良养生建议"+str(j+1)+"：" + i)

sql = "select HOSTITALNAME from DOCTORHOSTITALADRREST where PROVINCE='%s' and ADMINISTRATIVE='%s'" % (userinfo[3],userinfo[4])

cursor.execute(sql)

rows = cursor.fetchall()

yiyuan = []

for row in rows:

    yiyuan.append(row[0])

for i in yiyuan:

    print("系统智能匹配你所在地区附件的医院："+i)

sql = "select ADDRACTION,NAME,SUMMARY from DOCTORS where FAMILY='%s'" % (ke)

cursor.execute(sql)

rows = cursor.fetchall()

yisheng = []

for row in rows:

    yisheng.append(row[0])

    yisheng.append(row[1])

    yisheng.append(row[2])

print("系统为你推荐全国相关出色医生所在医院信息："+yisheng[0])

print("系统为你推荐全国相关出色医生姓名信息："+yisheng[1])

print("系统为你推荐全国相关出色医生简介信息："+yisheng[2])

yisheng_str = ""

yisheng_str += "医生所在医院："+yisheng[0]

yisheng_str += "医生姓名："+yisheng[1]

yisheng_str += "医生简介："+yisheng[2]

sql = "select CHACKPRO from CHACKPROJECT where FAMILY='%s'" % (ke)

cursor.execute(sql)

rows = cursor.fetchall()

jiancha = []

for row in rows:

    jiancha.append(row[0])

print("系统建议你到相关正规医院检查以下身体指标："+jiancha[0])

jianchax = ""

jianchax += "系统建议你到相关正规医院检查以下身体指标："+jiancha[0]

sql = "insert into zhenduanjilutable (userid,username,sex,age,province,area,bumen,ke,result,chufang,jianyi,yiyuaan,yisheng,jianchaxiang) values (%d,'%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')" % (uid,userinfo[0],userinfo[1],userinfo[2],userinfo[3],userinfo[4],bumen,ke,firstResult,chufang_str,jianyi_str,yiyuan[0],yisheng_str,jianchax)

cursor.execute(sql)

conn.commit()

print("此次智能诊断完成，欢迎你下次继续使用：天生自然健康智能医疗系统！")

print("特别提醒、注意：该系统的所有诊断只是作为参考，有必需要的用户请到相关正规医院接受相关专家医生完成检查、治疗等流程...")

print("系统建议：保持一颗善良、沉稳、宁静和广博的平常心度过每一个清晨和夜晚...")

print("祝你们每一位人都开开心心、健健康康、平平安安...阖家安康，如意吉祥......")

吴裕雄 python 人工智能——智能医疗系统后台用户注册、登录和初诊简约版代码展示的更多相关文章

  1. 吴裕雄 python 人工智能——智能医疗系统后台用户复诊模块简约版代码展示

    #复诊 import sys import os import time import operator import cx_Oracle import numpy as np import pand ...

  2. 吴裕雄 PYTHON 人工智能——智能医疗系统后台智能分诊模块及系统健康养生公告简约版代码展示

    #coding:utf-8 import sys import cx_Oracle import numpy as np import pandas as pd import tensorflow a ...

  3. 吴裕雄 python 人工智能——基于神经网络算法在智能医疗诊断中的应用探索代码简要展示

    #K-NN分类 import os import sys import time import operator import cx_Oracle import numpy as np import ...

  4. 吴裕雄 PYTHON 人工智能——基于MASK_RCNN目标检测（5）

    import os import sys import numpy as np import tensorflow as tf import matplotlib import matplotlib. ...

  5. 吴裕雄 PYTHON 人工智能——基于MASK_RCNN目标检测（4）

    import os import sys import random import math import re import time import numpy as np import tenso ...

  6. 吴裕雄 python 人工智能——基于Mask_RCNN目标检测（3）

    import os import sys import random import math import re import time import numpy as np import cv2 i ...

  7. 吴裕雄 python 人工智能——基于Mask_RCNN目标检测（2）

    import os import sys import itertools import math import logging import json import re import random ...

  8. 吴裕雄 python 人工智能——基于Mask_RCNN目标检测（1）

    import os import sys import random import math import numpy as np import skimage.io import matplotli ...

  9. 吴裕雄--python学习笔记：爬虫基础

    一.什么是爬虫 爬虫:一段自动抓取互联网信息的程序,从互联网上抓取对于我们有价值的信息. 二.Python爬虫架构 Python 爬虫架构主要由五个部分组成,分别是调度器.URL管理器.网页下载器.网 ...

随机推荐

  1. 动图演示23个鲜为人知的VSCode快捷键

    动图演示23个鲜为人知的VSCode快捷键 原文地址:dev.to/devmount/23… 代码同步浏览器 安装vccode 安装live server插件 尽管我在VS Code中经常使用许多快捷 ...

  2. String Buffer和String Builder(String类深入理解)

      String在Java里面JDK1.8后它属于一个特殊的类,在创建一个String基本对象的时候,String会向“ 字符串常量池(String constant pool)” 进行检索是否有该数 ...

  3. Spring boot --- 自动配置

    spring boot 自动配置 指的是针对很多spring 应用程序常见的应用功能,spring boot 能自动提供相关配置. spring boot 自动配置加载     Spring boot ...

  4. HTTP状态码详解【转】

    HTTP状态码,我都是现查现用. 我以前记得几个常用的状态码,比如200,302,304,404, 503. 一般来说我也只需要了解这些常用的状态码就可以了.  如果是做AJAX,REST,网络爬虫, ...

  5. Binary Number（位运算）

    #include<bits/stdc++.h> using namespace std; int n; int getBits1(int n)//求取一个数的二进制形式中1的个数. { i ...

  6. python3练习100题——022

    为了周末轻松点,多做一些题. 原题链接:http://www.runoob.com/python/python-exercise-example22.html 题目:两个乒乓球队进行比赛,各出三人.甲 ...

  7. 易错之 Java字符串比较

    字符串比较 不能直接用==判断,因为字符串内存地址不同,等号比较的是地址而不是大小 用equals()判断字符串是否相等 还可以用compareTo()比较

  8. java中成员变量，局部变量，静态变量的辨析

    转自百度:https://baijiahao.baidu.com/s?id=1625360816541592483&wfr=spider&for=pc 1.java中成员变量,局部变量 ...

  9. java 快捷表达式

    :: 和 ->  主要说这两个,这个好像也叫:Lambda表达式    但我不知道对不对,就先叫他 “快捷表达式“,顾名思义:使用它,可以省很多代码,可以用来装X,但不便于阅读. 这种东西呢,虽 ...

  10. Codeforces Round #530 (Div. 2) D. Sum in the tree 树上贪心

    D. Sum in the tree 题意 给出一颗树,奇数层数的点有值,值代表从1到该点的简单路的权值的和,偶数层数的点权值被擦去了 问所有节点的和的最小可能是多少 思路 对于每一个-1(也就是值未 ...