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| from tkinter import * | |
| import numpy as np | |
| import pandas as pd | |
| #List of the symptoms is listed here in list l1. | |
| l1=['back_pain','constipation','abdominal_pain','diarrhoea','mild_fever','yellow_urine', | |
| 'yellowing_of_eyes','acute_liver_failure','fluid_overload','swelling_of_stomach', | |
| 'swelled_lymph_nodes','malaise','blurred_and_distorted_vision','phlegm','throat_irritation', | |
| 'redness_of_eyes','sinus_pressure','runny_nose','congestion','chest_pain','weakness_in_limbs', | |
| 'fast_heart_rate','pain_during_bowel_movements','pain_in_anal_region','bloody_stool', | |
| 'irritation_in_anus','neck_pain','dizziness','cramps','bruising','obesity','swollen_legs', | |
| 'swollen_blood_vessels','puffy_face_and_eyes','enlarged_thyroid','brittle_nails', | |
| 'swollen_extremeties','excessive_hunger','extra_marital_contacts','drying_and_tingling_lips', | |
| 'slurred_speech','knee_pain','hip_joint_pain','muscle_weakness','stiff_neck','swelling_joints', | |
| 'movement_stiffness','spinning_movements','loss_of_balance','unsteadiness', | |
| 'weakness_of_one_body_side','loss_of_smell','bladder_discomfort','foul_smell_of urine', | |
| 'continuous_feel_of_urine','passage_of_gases','internal_itching','toxic_look_(typhos)', | |
| 'depression','irritability','muscle_pain','altered_sensorium','red_spots_over_body','belly_pain', | |
| 'abnormal_menstruation','dischromic _patches','watering_from_eyes','increased_appetite','polyuria','family_history','mucoid_sputum', | |
| 'rusty_sputum','lack_of_concentration','visual_disturbances','receiving_blood_transfusion', | |
| 'receiving_unsterile_injections','coma','stomach_bleeding','distention_of_abdomen', | |
| 'history_of_alcohol_consumption','fluid_overload','blood_in_sputum','prominent_veins_on_calf', | |
| 'palpitations','painful_walking','pus_filled_pimples','blackheads','scurring','skin_peeling', | |
| 'silver_like_dusting','small_dents_in_nails','inflammatory_nails','blister','red_sore_around_nose', | |
| 'yellow_crust_ooze'] | |
| #List of Diseases is listed in list disease. | |
| disease=['Fungal infection','Allergy','GERD','Chronic cholestasis','Drug Reaction', | |
| 'Peptic ulcer diseae','AIDS','Diabetes','Gastroenteritis','Bronchial Asthma','Hypertension', | |
| ' Migraine','Cervical spondylosis', | |
| 'Paralysis (brain hemorrhage)','Jaundice','Malaria','Chicken pox','Dengue','Typhoid','hepatitis A', | |
| 'Hepatitis B','Hepatitis C','Hepatitis D','Hepatitis E','Alcoholic hepatitis','Tuberculosis', | |
| 'Common Cold','Pneumonia','Dimorphic hemmorhoids(piles)', | |
| 'Heartattack','Varicoseveins','Hypothyroidism','Hyperthyroidism','Hypoglycemia','Osteoarthristis', | |
| 'Arthritis','(vertigo) Paroymsal Positional Vertigo','Acne','Urinary tract infection','Psoriasis', | |
| 'Impetigo'] | |
| l2=[] | |
| for i in range(0,len(l1)): | |
| l2.append(0) | |
| df=pd.read_csv("Prototype.csv") | |
| #Replace the values in the imported file by pandas by the inbuilt function replace in pandas. | |
| df.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4, | |
| 'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10, | |
| 'Migraine':11,'Cervical spondylosis':12, | |
| 'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19, | |
| 'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25, | |
| 'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31, | |
| 'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35, | |
| '(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39, | |
| 'Impetigo':40}},inplace=True) | |
| #check the df | |
| #print(df.head()) | |
| X= df[l1] | |
| #print(X) | |
| y = df[["prognosis"]] | |
| np.ravel(y) | |
| #print(y) | |
| #Read a csv named Testing.csv | |
| tr=pd.read_csv("Prototype-1.csv") | |
| #Use replace method in pandas. | |
| tr.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4, | |
| 'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10, | |
| 'Migraine':11,'Cervical spondylosis':12, | |
| 'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19, | |
| 'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25, | |
| 'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31, | |
| 'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35, | |
| '(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39, | |
| 'Impetigo':40}},inplace=True) | |
| X_test= tr[l1] | |
| y_test = tr[["prognosis"]] | |
| #print(y_test) | |
| np.ravel(y_test) | |
| def DecisionTree(): | |
| from sklearn import tree | |
| clf3 = tree.DecisionTreeClassifier() | |
| clf3 = clf3.fit(X,y) | |
| from sklearn.metrics import accuracy_score | |
| y_pred=clf3.predict(X_test) | |
| print(accuracy_score(y_test, y_pred)) | |
| print(accuracy_score(y_test, y_pred,normalize=False)) | |
| psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()] | |
| for k in range(0,len(l1)): | |
| for z in psymptoms: | |
| if(z==l1[k]): | |
| l2[k]=1 | |
| inputtest = [l2] | |
| predict = clf3.predict(inputtest) | |
| predicted=predict[0] | |
| h='no' | |
| for a in range(0,len(disease)): | |
| if(predicted == a): | |
| h='yes' | |
| break | |
| if (h=='yes'): | |
| t1.delete("1.0", END) | |
| t1.insert(END, disease[a]) | |
| else: | |
| t1.delete("1.0", END) | |
| t1.insert(END, "Not Found") | |
| def randomforest(): | |
| from sklearn.ensemble import RandomForestClassifier | |
| clf4 = RandomForestClassifier() | |
| clf4 = clf4.fit(X,np.ravel(y)) | |
| # calculating accuracy | |
| from sklearn.metrics import accuracy_score | |
| y_pred=clf4.predict(X_test) | |
| print(accuracy_score(y_test, y_pred)) | |
| print(accuracy_score(y_test, y_pred,normalize=False)) | |
| psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()] | |
| for k in range(0,len(l1)): | |
| for z in psymptoms: | |
| if(z==l1[k]): | |
| l2[k]=1 | |
| inputtest = [l2] | |
| predict = clf4.predict(inputtest) | |
| predicted=predict[0] | |
| h='no' | |
| for a in range(0,len(disease)): | |
| if(predicted == a): | |
| h='yes' | |
| break | |
| if (h=='yes'): | |
| t2.delete("1.0", END) | |
| t2.insert(END, disease[a]) | |
| else: | |
| t2.delete("1.0", END) | |
| t2.insert(END, "Not Found") | |
| def NaiveBayes(): | |
| from sklearn.naive_bayes import GaussianNB | |
| gnb = GaussianNB() | |
| gnb=gnb.fit(X,np.ravel(y)) | |
| from sklearn.metrics import accuracy_score | |
| y_pred=gnb.predict(X_test) | |
| print(accuracy_score(y_test, y_pred)) | |
| print(accuracy_score(y_test, y_pred,normalize=False)) | |
| psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()] | |
| for k in range(0,len(l1)): | |
| for z in psymptoms: | |
| if(z==l1[k]): | |
| l2[k]=1 | |
| inputtest = [l2] | |
| predict = gnb.predict(inputtest) | |
| predicted=predict[0] | |
| h='no' | |
| for a in range(0,len(disease)): | |
| if(predicted == a): | |
| h='yes' | |
| break | |
| if (h=='yes'): | |
| t3.delete("1.0", END) | |
| t3.insert(END, disease[a]) | |
| else: | |
| t3.delete("1.0", END) | |
| t3.insert(END, "Not Found") | |
| # GUI stuff.............................................................................. | |
| root = Tk() | |
| root.configure(background='white') | |
| Symptom1 = StringVar() | |
| Symptom1.set("Select Here") | |
| Symptom2 = StringVar() | |
| Symptom2.set("Select Here") | |
| Symptom3 = StringVar() | |
| Symptom3.set("Select Here") | |
| Symptom4 = StringVar() | |
| Symptom4.set("Select Here") | |
| Symptom5 = StringVar() | |
| Symptom5.set("Select Here") | |
| Name = StringVar() | |
| w2 = Label(root, justify=LEFT, text="Disease Predictor using Machine Learning", fg="Blue", bg="White") | |
| w2.config(font=("Times",30,"bold ")) | |
| w2.grid(row=1, column=0, columnspan=2, padx=100) | |
| w2 = Label(root, justify=LEFT, text="A Project by C N G", fg="Black", bg="Grey") | |
| w2.config(font=("Times",30,"bold ")) | |
| w2.grid(row=2, column=0, columnspan=2, padx=100) | |
| NameLb = Label(root, text="Name of the Patient", fg="Black", bg="grey") | |
| NameLb.config(font=("Times",15,"bold italic")) | |
| NameLb.grid(row=6, column=0, pady=15, sticky=W) | |
| S1Lb = Label(root, text="Symptom 1", fg="Black", bg="grey") | |
| S1Lb.config(font=("Times",15,"bold italic")) | |
| S1Lb.grid(row=7, column=0, pady=10, sticky=W) | |
| S2Lb = Label(root, text="Symptom 2", fg="Black", bg="grey") | |
| S2Lb.config(font=("Times",15,"bold italic")) | |
| S2Lb.grid(row=8, column=0, pady=10, sticky=W) | |
| S3Lb = Label(root, text="Symptom 3", fg="Black",bg="grey") | |
| S3Lb.config(font=("Times",15,"bold italic")) | |
| S3Lb.grid(row=9, column=0, pady=10, sticky=W) | |
| S4Lb = Label(root, text="Symptom 4", fg="Black", bg="grey") | |
| S4Lb.config(font=("Times",15,"bold italic")) | |
| S4Lb.grid(row=10, column=0, pady=10, sticky=W) | |
| S5Lb = Label(root, text="Symptom 5", fg="Black", bg="grey") | |
| S5Lb.config(font=("Times",15,"bold italic")) | |
| S5Lb.grid(row=11, column=0, pady=10, sticky=W) | |
| lrLb = Label(root, text="DecisionTree", fg="white", bg="red") | |
| lrLb.config(font=("Times",15,"bold italic")) | |
| lrLb.grid(row=15, column=0, pady=10,sticky=W) | |
| destreeLb = Label(root, text="RandomForest", fg="Red", bg="silver") | |
| destreeLb.config(font=("Times",15,"bold italic")) | |
| destreeLb.grid(row=17, column=0, pady=10, sticky=W) | |
| ranfLb = Label(root, text="NaiveBayes", fg="White", bg="green") | |
| ranfLb.config(font=("Times",15,"bold italic")) | |
| ranfLb.grid(row=19, column=0, pady=10, sticky=W) | |
| OPTIONS = sorted(l1) | |
| NameEn = Entry(root, textvariable=Name) | |
| NameEn.grid(row=6, column=1) | |
| S1 = OptionMenu(root, Symptom1,*OPTIONS) | |
| S1.grid(row=7, column=1) | |
| S2 = OptionMenu(root, Symptom2,*OPTIONS) | |
| S2.grid(row=8, column=1) | |
| S3 = OptionMenu(root, Symptom3,*OPTIONS) | |
| S3.grid(row=9, column=1) | |
| S4 = OptionMenu(root, Symptom4,*OPTIONS) | |
| S4.grid(row=10, column=1) | |
| S5 = OptionMenu(root, Symptom5,*OPTIONS) | |
| S5.grid(row=11, column=1) | |
| dst = Button(root, text="Prediction 1", command=DecisionTree,bg="Red",fg="Black") | |
| dst.config(font=("Times",15,"bold italic")) | |
| dst.grid(row=8, column=3,padx=10) | |
| rnf = Button(root, text="Prediction 2", command=randomforest,bg="White",fg="Black") | |
| rnf.config(font=("Times",15,"bold italic")) | |
| rnf.grid(row=9, column=3,padx=10) | |
| lr = Button(root, text="Prediction 3", command=NaiveBayes,bg="Green",fg="Black") | |
| lr.config(font=("Times",15,"bold italic")) | |
| lr.grid(row=10, column=3,padx=10) | |
| t1 = Text(root, height=1, width=40,bg="white",fg="red") | |
| t1.config(font=("Times",15,"bold italic")) | |
| t1.grid(row=15, column=1, padx=10) | |
| t2 = Text(root, height=1, width=40,bg="White",fg="Blue") | |
| t2.config(font=("Times",15,"bold italic")) | |
| t2.grid(row=17, column=1 , padx=10) | |
| t3 = Text(root, height=1, width=40,bg="white",fg="white") | |
| t3.config(font=("Times",15,"bold italic")) | |
| t3.grid(row=19, column=1 , padx=10) | |
| root.mainloop() |