Add Sleep Disorder Predictor Model

App.py

@@ -130,6 +130,18 @@
             - **spread1**, **spread2**, **PPE**:  Nonlinear measures of fundamental frequency variation.
         """
     },
+    {
+    "name": "Sleep Disorder Prediction",
+    "description": "Assess your risk of developing a sleep disorder using advanced ML algorithms.",
+    "details": """
+        ### Introduction
+        Sleep disorders can have a significant impact on an individual's overall health and well-being. These disorders often result from a combination of poor sleep habits, lifestyle factors, stress, and underlying medical conditions.
+
+        ### Sleep Health and Lifestyle Dataset
+        The dataset consists of sleep, lifestyle, and health metrics collected from 400 individuals. The main goal is to predict the likelihood of an individual having a sleep disorder using the "Sleep Disorder" column, which contains categorical values indicating the presence or absence of a sleep disorder.
+
+        """
+},
     {
         "name": "PDF Malware Detector",
         "description": "Identify and alert users about potential malware in PDF files.",

form_configs/sleep_prediction.json

@@ -0,0 +1,105 @@
+{
+    "Sleep Prediction Form": {
+        "Age": {
+            "field_name": "Age",
+            "type": "number",
+            "min_value": 0,
+            "max_value": 120,
+            "default_value": 25,
+            "step": 1
+        },
+        "Sleep_Duration": {
+            "field_name": "Sleep_Duration",
+            "type": "float",
+            "min_value": 0.0,
+            "max_value": 24.0,
+            "default_value": 8.0,
+            "step": 0.1
+        },
+        "Heart_Rate": {
+            "field_name": "Heart_Rate",
+            "type": "number",
+            "min_value": 10,
+            "max_value": 200,
+            "default_value": 72,
+            "step": 1
+        },
+        "Daily_Steps": {
+            "field_name": "Daily_Steps",
+            "type": "number",
+            "min_value": 0,
+            "max_value": 1000000,
+            "default_value": 0,
+            "step": 10
+        },
+        "Systolic": {
+            "field_name": "Systolic",
+            "type": "float",
+            "min_value": 0.0,
+            "max_value": 250.0,
+            "default_value": 120.0,
+            "step": 0.1
+        },
+        "Diastolic": {
+            "field_name": "Diastolic",
+            "type": "float",
+            "min_value": 0.0,
+            "max_value": 250.0,
+            "default_value": 80.0,
+            "step": 0.1
+        },
+        "Occupation": {
+            "field_name": "Occupation",
+            "type": "dropdown",
+            "options": [
+                "Software Engineer" ,"Doctor" ,"Sales Representative","Teacher" ,"Nurse",
+ "Engineer" ,"Accountant" ,"Scientist", "Lawyer" ,"Salesperson" ,"Manager"
+            ],
+            "default_value": "Software Engineer" 
+        },
+        "Quality_of_Sleep": {
+            "field_name": "Quality_of_Sleep",
+            "type": "number",
+            "min_value": 0,
+            "max_value": 10,
+            "default_value": 0,
+            "step": 1
+        },
+        "Gender": {
+            "field_name": "Gender",
+            "type": "dropdown",
+            "options": [
+                "Male",
+                "Female"
+            ],
+            "default_value": "Male"
+
+        },
+        "Physical_Activity_Level": {
+            "field_name": "Physical_Activity_Level",
+            "type": "number",
+            "min_value": 0,
+            "max_value": 200,
+            "default_value": 0,
+            "step": 1
+        },
+        "Stress_Level": {
+            "field_name": "Stress_Level",
+            "type": "number",
+            "min_value": 0,
+            "max_value": 10,
+            "default_value": 0,
+            "step": 1
+        },
+        "BMI_Category": {
+            "field_name": "BMI_Category",
+            "type": "dropdown",
+            "options": [
+                "Normal Weight",
+                "Obese",
+                "Overweight"
+            ],
+            "default_value": "Normal Weight"
+        }
+    }
+}

models/sleep_disorder_predictor/model.py

@@ -0,0 +1,43 @@
+import streamlit as st
+import pickle
+import pandas as pd  # Import pandas to handle DataFrames
+import numpy as np
+import warnings
+warnings.filterwarnings("ignore")
+
+# Load the model and the scaler
+model_path = 'models/sleep_disorder_predictor/saved_models/Model_Prediction.sav'
+preprocessor_path = 'models/sleep_disorder_predictor/saved_models/preprocessor.sav'
+
+# Load the pre-trained model and scaler using pickle
+loaded_model = pickle.load(open(model_path, 'rb'))
+preprocessor = pickle.load(open(preprocessor_path, 'rb'))
+
+# Define the prediction function
+def disease_get_prediction(Age, Sleep_Duration,  
+                            Heart_Rate, Daily_Steps, 
+                            Systolic, Diastolic, Occupation, Quality_of_Sleep, Gender, 
+                            Physical_Activity_Level, Stress_Level, BMI_Category):
+    # Create a DataFrame with the features using correct column names
+    features = pd.DataFrame({
+        'Age': [int(Age)],
+        'Sleep Duration': [float(Sleep_Duration)],  # Changed to match expected name
+        'Heart Rate': [int(Heart_Rate)],            # Changed to match expected name
+        'Daily Steps': [int(Daily_Steps)],          # Changed to match expected name
+        'Systolic': [float(Systolic)],
+        'Diastolic': [float(Diastolic)],
+        'Occupation': [Occupation],
+        'Quality of Sleep': [int(Quality_of_Sleep)], # Changed to match expected name
+        'Gender': [Gender],
+        'Physical Activity Level': [int(Physical_Activity_Level)], # Changed to match expected name
+        'Stress Level': [int(Stress_Level)],       # Changed to match expected name
+        'BMI Category': [BMI_Category]               # Changed to match expected name
+    })
+
+    # Apply the preprocessor (make sure it expects a DataFrame)
+    preprocessed_data = preprocessor.transform(features)
+
+    # Make prediction
+    prediction = loaded_model.predict(preprocessed_data)
+
+    return prediction

models/sleep_disorder_predictor/predict.py

@@ -0,0 +1,25 @@
+from models.sleep_disorder_predictor.model import disease_get_prediction
+
+def get_prediction(Age, Sleep_Duration,  
+                    Heart_Rate, Daily_Steps, 
+                    Systolic, Diastolic,Occupation,Quality_of_Sleep,Gender, 
+                    Physical_Activity_Level, Stress_Level, BMI_Category):
+
+    prediction = disease_get_prediction(Age, Sleep_Duration,  
+                    Heart_Rate, Daily_Steps, 
+                    Systolic, Diastolic,Occupation,Quality_of_Sleep,Gender, 
+                    Physical_Activity_Level, Stress_Level, BMI_Category)
+
+    message = ""
+
+    # Provide message based on the prediction value
+    if prediction==0:
+        message= "Insomnia"
+    elif prediction==1:
+        message = "No disorder"
+    elif prediction==2:
+        message = "Sleep Apnea"
+    else:
+        message="Invalid details."
+
+    return message+"\n\nRecommendation - To prevent sleep disorders, maintain a balanced lifestyle with regular exercise, a healthy diet, and stress management. Stick to a consistent sleep schedule, limit caffeine and alcohol, and create a relaxing bedtime routine."

pages/Sleep_Disorder_Predictor.py

@@ -0,0 +1,4 @@
+from page_handler import PageHandler
+
+page_handler = PageHandler("pages/pages.json")
+page_handler.render_page("Sleep Disorder Predictor")

pages/pages.json

@@ -152,6 +152,28 @@
       }
     ]
   },
+  "Sleep Disorder Predictor": {
+    "title": "Sleep Disorder Predictor",
+    "page_title": "Sleep Disorder Predictor",
+    "model_predict_file_path": "models/sleep_disorder_predictor/predict.py",
+    "model_function": "get_prediction",
+    "model_detail_function": "model_details",
+    "form_config_path": "form_configs/sleep_prediction.json",
+    "tabs": [
+      {
+        "name": "Sleep Disorder Predictor",
+        "type": "form",
+        "form_name": "Sleep Prediction Form"
+      },
+      {
+        "name": "Model Details",
+        "type": "model_details",
+
+          "problem_statement": "The model aims to predict the likelihood of an individual having a sleep disorder based on lifestyle, sleep quality, and health metrics.",
+          "description": "Using a XGBoost classifier, the model predicts whether an individual is likely to have a sleep disorder based on features such as sleep duration, stress level, physical activity level, cardiovascular health metrics, and demographic information."
+     }
+    ]
+  },
 
   "Malware_Detection": {
   "title": "PDF Malware Detection",