IDENTIFICATION AND ANALYSIS OF NUMEROUS DISEASES USING SUPERVISED MACHINE LEARNING TECHNIQUES AND FLASK API

Abstract

Many of the existing machine learning models for health care analysis are concentrating on one disease per
analysis. Like one analysis if for diabetes analysis, one for cancer analysis, one for skin diseases like that. There
is no common system where one analysis can perform more than one disease prediction. In this project proposing
a system which used to predict multiple diseases by using Flask API. In this project used to analyse Diabetes
analysis, liver disease analysis, heart disease and breast cancer analysis. Later other diseases like skin diseases,
fever analysis and many more diseases can be included. To implement multiple disease analysis used machine
learning algorithms, tensor flow and Flask API. Python pickling is used to save the model behaviour and python
unpickling is used to load the pickle file whenever required. The importance of this project analysis in while
analysing the diseases all the parameters which causes the disease is included so it possible to detect the maximum
effects which the disease will cause. For example, for diabetes analysis in many existing systems considered few
parameters like age, sex, bmi, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies,
considered in addition to age, sex, bmi, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies
included serum creatinine, potassium, Glasgow Coma Scale, heart rate/pulse Rate, respiration rate, body
temperature, low density lipoprotein (LDL), high density lipoprotein (HDL), TG (Triglycerides). Final models
behaviour will be saved as python pickle file. Flask API is designed. When user accessing this API, the user has
to send the parameters of the disease along with disease name. Flask API will invoke the corresponding model
and returns the status of the patient. The importance of this analysis to analyse the maximum diseases, so that to
monitor the patient’s condition and warn the patients in advance to decrease mortality ratio.