import tkinter as tk
from tkinter import ttk
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Function to train models
def train_models(X_train_scaled, y_train):
# Train Support Vector Machine (SVM) classifier
svm_clf = SVC(kernel='linear', random_state=42)
svm_clf.fit(X_train_scaled, y_train)
# Train Random Forest classifier
rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rf_clf.fit(X_train_scaled, y_train)
return svm_clf, rf_clf
# Function to predict asthma
def predict_asthma(input_values, scaler, svm_clf, rf_clf):
input_df = pd.DataFrame([input_values], columns=labels)
input_scaled = scaler.transform(input_df)
svm_pred = svm_clf.predict(input_scaled)
rf_pred = rf_clf.predict(input_scaled)
return svm_pred, rf_pred
# Create the main window
root = tk.Tk()
root.title("Asthma Detection System")
# Create a frame for the main content
main_frame = ttk.Frame(root, padding="20")
main_frame.grid(row=0, column=0, sticky="nsew")
# Create labels and entry fields for the input features
labels = ['Age ( Above 50 → 5 ,41-50 → 4 ,19-30 → 3 ,31-40 → 2 )', 'Gender ( Male → 1 , Female → 0)', 'OutdoorJob ( Rarely → 0 ,Occasionally → 1 ,Frequently → 2)', 'OutdoorActivities (Extremely likely → 1 , Neither likely nor unlikely → 2 , Not at all likely → 0)', 'SmokingHabit (Yes → 1 , No → 0)',
'Humidity', 'Pressure', 'Temperature', 'UVIndex (Extreme → 1 , Low → 0)', 'WindSpeed']
entries = {}
for i, label in enumerate(labels):
ttk.Label(main_frame, text=label).grid(row=i, column=0, sticky="w")
entries[label] = ttk.Entry(main_frame)
entries[label].grid(row=i, column=1, sticky="ew")
# Load data and train models
data = pd.read_excel("C:/Users/saket/Downloads/modified_dataset (1).xlsx")
X = data[['Age ( Above 50 → 5 ,41-50 → 4 ,19-30 → 3 ,31-40 → 2 )', 'Gender ( Male → 1 , Female → 0)', 'OutdoorJob ( Rarely → 0 ,Occasionally → 1 ,Frequently → 2)', 'OutdoorActivities (Extremely likely → 1 , Neither likely nor unlikely → 2 , Not at all likely → 0)', 'SmokingHabit (Yes → 1 , No → 0)',
'Humidity', 'Pressure', 'Temperature', 'UVIndex (Extreme → 1 , Low → 0)', 'WindSpeed']]
y = data['ACTScore'] # Assuming 'ACTScore' is the target variable
X_train, _, y_train, _, = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled, _, y_train, _, = train_test_split(X, y, test_size=0.2, random_state=42)
svm_clf, rf_clf = train_models(scaler.transform(X_train_scaled), y_train)
# Function to get input values and display results
def display_prediction():
input_values = [float(entries[label].get()) for label in labels]
svm_pred, rf_pred = predict_asthma(input_values, scaler, svm_clf, rf_clf)
svm_label.config(text=f"SVM Prediction: {svm_pred}")
rf_label.config(text=f"Random Forest Prediction: {rf_pred}")
avg_pred=(rf_pred+svm_pred)/2
if 5 <= avg_pred <= 10:
show_medicines_for_score_5_to_10()
elif 10 < avg_pred <= 19:
show_medicines_for_score_10_to_19()
elif avg_pred > 19:
show_medicines_for_score_above_19()
# Create a button to trigger prediction
predict_button = ttk.Button(main_frame, text="Predict", command=display_prediction)
predict_button.grid(row=len(labels)+1, columnspan=2, pady=10)
# Create labels to display prediction results
svm_label = ttk.Label(main_frame, text="")
svm_label.grid(row=len(labels)+2, columnspan=2, pady=5)
rf_label = ttk.Label(main_frame, text="")
rf_label.grid(row=len(labels)+3, columnspan=2, pady=5)
def show_medicines_for_score_5_to_10():
info_text.set("For ACT Score 5 to 10:\n\n"
"Medicines:\n"
"- Short-Acting Beta Agonists (SABAs)\n"
"- Oral Corticosteroids\n"
"- Theophylline\n\n"
"Natural Remedies:\n"
"- Breathing Exercises\n"
"- Herbal Remedies\n"
"- Honey and Ginger Tea\n\n"
"Rare but serious side effects:\n"
"-Rapid or irregular heartbeat (palpitations)\n"
"-Tremor\n"
"-Nervousness or anxiety\n"
"-Dizziness\n"
"-Muscle cramps\n")
def show_medicines_for_score_10_to_19():
info_text.set("For ACT Score 10 to 19:\n\n"
"Medicines:\n"
"- Inhaled Corticosteroids (ICS)\n"
"- Combination Inhalers\n"
"- Leukotriene Modifiers\n\n"
"Natural Remedies:\n"
"- Breathing Exercises\n"
"- Yoga\n"
"- Omega-3 Fatty Acids\n"
"- Quercetin\n\n"
"Rare but serious side effects:\n"
"-Adrenal insufficiency (especially with long-term use of high doses)\n"
"-Osteoporosis (bone thinning)\n"
"-Growth suppression in children\n"
"-Glaucoma or cataracts (with prolonged use at high doses)\n"
"-Increased risk of infections\n")
def show_medicines_for_score_above_19():
info_text.set("For ACT Score >19:\n\n"
"Medicines:\n"
"- Long-Acting Beta Agonists (LABAs)\n"
"- Biologic Therapies\n"
"- Oral Corticosteroids (for exacerbations)\n\n"
"Natural Remedies:\n"
"- Breathing Exercises\n"
"- Yoga\n"
"- Acupuncture\n"
"- Lifestyle Modifications\n\n"
"Rare but serious side effects:\n"
"-Sore throat\n"
"-Hoarseness or voice changes\n"
"-Thrush (oral fungal infection)\n"
"-Cough\n"
"-Headache\n")
# Information Text
info_text = tk.StringVar()
info_label = ttk.Label(main_frame, textvariable=info_text, wraplength=500)
info_label.grid(row=20, columnspan=2)
# Run the main event loop
root.mainloop()
Datasets
Models
