machinelearning.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sklearn
import pickle


def most_frequent(List):
    counter = 0
    num = List[0]
     
    for i in List:
        curr_frequency = List.count(i)
        if(curr_frequency> counter):
            counter = curr_frequency
            num = i
 
    return num

symptoms = ['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']

diseases = ['Fungal infection', 'Allergy', 'GERD', 'Chronic cholestasis', 'Drug Reaction', 'Peptic ulcerdiseae', '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']

def predict(lst):
	num_list = []
	for i in symptoms:
		if i in lst:
			num_list.append(1)
		else:
			num_list.append(0)
	return [num_list]

def prediction(lst):
	answer = ""
	descisionTreeModel = pickle.load(open("models/desicionTree.model", "rb"))
	naiveBayesModel = pickle.load(open("models/naiveBayes.model", 'rb'))
	randomForestModel = pickle.load(open("models/randomForest.model", 'rb'))
	answer1 = descisionTreeModel.predict(predict(lst))
	answer2 = randomForestModel.predict(predict(lst))
	answer3 = naiveBayesModel.predict(predict(lst))

	answer = [answer1, answer2, answer3]
	return diseases[most_frequent(answer)[0]]