diabetes disease prediction

Author: adityajai25

MachineLearning Projects/Diabetes-disease-Prediction/Dataset/diabetes.csv

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+# Disease prediction

MachineLearning Projects/Diabetes-disease-Prediction/Model/modelForPrediction.pkl

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+from wsgiref import simple_server
+from flask import Flask, request, app,render_template
+from flask import Response
+from flask_cors import CORS
+import pickle
+import bz2
+import datetime
+import numpy as np
+import pandas as pd
+from flask import Flask, request, jsonify
+# from pymongo import MongoClient
+# from pymongo.server_api import ServerApi
+
+
+app = Flask(__name__)
+CORS(app)
+app.config['DEBUG'] = True
+
+scalarobject=bz2.BZ2File("Model/standardScalar.pkl", "rb")
+scaler=pickle.load(scalarobject)
+modelforpred = bz2.BZ2File("Model/modelForPrediction.pkl", "rb")
+model = pickle.load(modelforpred)
+
+## Mongo db connection
+# uri = "mongodb+srv://adityajai243:db2023@cluster0.buez9p5.mongodb.net/?retryWrites=true&w=majority"
+
+
+# # Create a new client and connect to the server
+# client = MongoClient(uri, server_api=ServerApi('1'))
+
+# # Send a ping to confirm a successful connection
+# try:
+#     client.admin.command('ping')
+#     print("Pinged your deployment. You successfully connected to MongoDB!")
+# except Exception as e:
+#     print(e)
+
+# # Define the database and collection
+# db = client['Patient']  # Database name
+# collection = db['patient-login']  # Collection name
+
+
+##Route for checking backend connection
+
+
+## Route for homepage
+
+@app.route('/',methods=['GET','POST'])
+def index():
+    return render_template('index.html')
+
+## Route for patient Registration
+@app.route('/add-patients',methods=['GET','POST'])
+def reg():
+    return render_template('patient-registration.html')
+
+
+## Route for Single data point prediction
+@app.route('/diabetes',methods=['GET','POST'])
+def predict_datapoint():
+    result=""
+    current_datetime = datetime.datetime.now()
+    f_datetime = current_datetime.strftime('%d-%m-%Y  %I:%M %p')
+
+    if request.method=='POST':
+        name=request.form.get("name")
+        Age = int(request.form.get('Age'))
+        gender = request.form.get('gender')
+        if gender=='Male':
+            Pregnancies=0
+            pregnancy='No'
+        if gender =='Female':
+            Pregnancies=int(request.form.get("Pregnancies"))
+            pregnancy=request.form.get("pregnancy")
+
+        Glucose = float(request.form.get('Glucose'))
+        BloodPressure = float(request.form.get('BloodPressure'))
+        SkinThickness = float(request.form.get('SkinThickness'))
+        Insulin = float(request.form.get('Insulin'))
+        BMI = float(request.form.get('BMI'))
+        DiabetesPedigreeFunction = float(request.form.get('DiabetesPedigreeFunction'))
+        
+        #classification
+        if Glucose > 200 and Insulin < 10:
+            classification = 'Type 1 Diabetes'
+        elif Glucose > 126 and Insulin >= 10:
+            classification = 'Type 2 Diabetes'
+        elif Glucose > 92:
+            classification = 'Gestational Diabetes'
+        else:
+            classification = 'Unclassified Diabetes'
+
+        new_data=scaler.transform([[Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age]])
+        predict=model.predict(new_data)
+       
+        if predict[0] ==1 :
+            result = 'Diabetic'
+        else:
+            result ='Non-Diabetic'
+            
+        return render_template('diabetes.html',result=result,name=name,Age=Age,BMI=BMI,Pregnancies=Pregnancies,Glucose=Glucose,BloodPressure=BloodPressure,Insulin=Insulin,DiabetesPedigreeFunction=DiabetesPedigreeFunction,SkinThickness=SkinThickness,gender=gender,Pregnancy=pregnancy,datetime=f_datetime,type=classification)
+
+    else:
+        return render_template('home.html')
+
+
+if __name__=="__main__":
+    app.run(host="0.0.0.0")
+    app.debug(True)

MachineLearning Projects/Diabetes-disease-Prediction/Model/standardScalar.pkl

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+{
+    "Pregnancies":6,
+    "Glucose":148,
+    "BloodPressure":72,
+    "SkinThickness":35,
+    "Insulin":0,
+    "BMI":33.6,
+    "DiabetesPedigreeFunction":0.62,
+    "Age":59
+    
+}

MachineLearning Projects/Diabetes-disease-Prediction/Notebook/Logistic_Regression.ipynb

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+certifi==2019.11.28
+Click==7.0
+Flask==1.1.1
+Flask-Cors==3.0.8
+gunicorn==20.0.4
+itsdangerous==1.1.0
+Jinja2==2.10.3
+joblib==0.14.1
+MarkupSafe==1.1.1
+numpy==1.18.0
+pandas==0.25.3
+python-dateutil==2.8.1
+pytz==2019.3
+scikit-learn==0.22
+scipy==1.4.1
+six==1.13.0
+Werkzeug==0.16.0
+wincertstore==0.2

MachineLearning Projects/Diabetes-disease-Prediction/README.md

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+body{
+    overflow-x: hidden;
+}
+.report{
+    display: flex;
+    justify-content: space-between;
+    border: 1px solid black;
+    background-color: #f0f0f0;
+    padding: 20px;
+}
+.left, .right {
+    padding: 20px;
+}
+.left {
+    width: 48%;
+    background-color: #fff;
+    border: 1px solid #ccc;
+    border-radius: 5px;
+}
+.right {
+    width: 48%;
+    background-color: #fff;
+    border: 1px solid #ccc;
+    border-radius: 5px;
+}
+
+
+h3 {
+    font-size: 24px;
+    text-decoration: underline;
+    margin-bottom: 15px;
+}
+p {
+    font-size: 16px;
+    margin-bottom: 10px;
+}
+
+.print:last-child {
+    display: flex;
+    justify-content: center;
+    margin-top: 20px;
+}
+
+button{
+    margin: 1rem;
+    padding: 0.5rem;
+    width: 30%;
+    border: 1px solid black;
+    background-color: #fff;
+    cursor: pointer;
+}
+
+button:hover{
+    background-color: #000;
+    color: #fff;
+}
+
+.positive{
+    color: green;
+}
+.ideal{
+    color: #DBA800;
+}
+.negative{
+    color: red;
+}
+
+.end p{
+    font-size: 16px;
+    text-align: center;
+}
+
+.diabetes {
+    background-color: #FFA0A0;
+    border-radius: 20px;
+    padding: 20px;
+    margin: 20px ;
+    width:auto;
+}
+
+.diabetes blockquote {
+    font-size: 18px;
+    font-style: italic;
+    margin: 20px 0;
+}
+
+.diabetes ul {
+    list-style: disc;
+    padding-left: 20px;
+}
+
+.diabetes li {
+    font-size: 16px;
+}
+
+.non-diabetic {
+    background-color: #A0FFA0;
+    border-radius: 20px;
+    padding: 20px;
+    margin: 20px ;
+    width:auto;
+}
+
+.non-diabetic blockquote {
+    font-size: 18px;
+    font-style: italic;
+    margin: 20px 0;
+}
+
+.non-diabetic ul {
+    list-style: disc;
+    padding-left: 20px;
+}
+
+.non-diabetic li {
+    font-size: 16px;
+}
+.print{
+    align-items: center;
+    justify-content: center;
+}
+.date{
+    margin-left: 1px;
+    padding:2px;
+}
+
+table {
+    width: 100%;
+    border:1px solid #000;
+    /* border-collapse: collapse; */
+    margin: 20px auto;
+}
+th, td {
+    border: 1px solid #000;
+    padding: 10px;
+    text-align: left;
+}
+th {
+    background-color: #333;
+    color: #fff;
+}
+tr:nth-child(even) {
+    background-color: #f2f2f2;
+}

MachineLearning Projects/Diabetes-disease-Prediction/app.py

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+body {
+  font-family: Arial, sans-serif;
+  /* background-image: linear-gradient(
+      to left top,
+      #04b4c4,
+      #04b4c4,
+      #a8eb12,
+      #a8eb12
+    ); */
+  min-height: 100vh;
+  background-size: cover;
+}
+
+.container {
+  max-width: 400px;
+  margin: 200px auto;
+  padding: 20px;
+  border: 1px solid #ccc;
+  border-radius: 5px;
+  box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
+}
+
+.form-group {
+  margin-bottom: 15px;
+}
+
+label {
+  display: block;
+  font-weight: bold;
+  margin-bottom: 5px;
+}
+
+input {
+  width: 70%;
+  padding: 10px;
+  border: 1px solid #ccc;
+  border-radius: 5px;
+  font-size: 16px;
+}
+
+button {
+  background-color: #007bff;
+  color: #fff;
+  padding: 10px 20px;
+  border: none;
+  border-radius: 5px;
+  cursor: pointer;
+}
+
+#error-message {
+  color: red;
+  margin-top: 10px;
+}