Multiple Disease Prediction Using Streamlit
Kesa Sruthi, Jadala Srilipi, Vodyati Vyshnavi, Dr Sreedhar Bhukya
Prediction, Random forest,Decision Tree, SVM Classifier, Exploratory Data Analysis, Machine Learning.
Wellbeing is one of the significant variables to be considered by an individual.Healthcare falls under the essential conveniences to be given to the society.Many of the current AI models for medical services examination are focusing on one disease prediction for each analysis.Our point is to anticipate the various sorts of illness in single stage by utilizing inbuilt python module Streamlit.In this task we are utilizing Naïve Bayes algorithm, random forest,decision tree and svm classifier are utilized for prediction of a particular disease .The calculation which gives more accuracy is used to train the data set before implementation.To implement multiple disease analysis used machine learning algorithms,Streamlit and python pickling is utilized to save the model behaviour.In this article we analyse Diabetes analysis,Heart disease and parkinson's disease by using some of the basic parameters such as Pulse Rate, Cholesterol, Blood Pressure, Heart Rate, etc., and also the risk factors associated with the disease can be found using prediction model with good accuracy and Precision.Further we can include other kind of chronic diseases,skin diseases and many other.In this work, demonstrated that using only core health parameters many diseases can be predicted.The significance of this analysis to analyse the maximum diseases to screen the patient's condition and caution the patients ahead of time to diminish mortality proportion.
Article Details
Unique Paper ID: 155579

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 1169 - 1172
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