Early Disease Perdition using Machine Learning


Early Disease Perdition using Machine Learning


Machine Learning techniques are used for a variety of applications. In the healthcare industry, Machine Learning plays an important role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using the Machine Learning technique the number of tests can be reduced. This reduced test plays an important role in time and performance. This paper analyses Machine Learning techniques which can be used for predicting different types of diseases which mainly concentrate on predicting Heart and Diabetes and Lung diseases. Early Disease Perdition using Machine Learning


Medical science has a large amount of data growth per year. Due to the increased amount of data growth in the medical and healthcare field the accurate analysis on medical data which has been benefiting from early patient care. With the help of disease data, data mining finds hidden pattern information in a huge amount of medical data. We proposed general disease prediction based on the symptoms of the patient.


The Disease Prediction application is end-user support and online consultation project. . It then processes user-specific details to check for various illnesses that could be associated with it. Here the scope of the project is that integration of clinical decision support with computer-based patient records could reduce medical errors, enhance patient safety, and decrease unwanted practice variation, and improve patient outcomes.

Existing system:

  • This paper is to address this important problem and design cloud-assisted privacy.
  • It preserves health records to protect the privacy of the involved parties and their data.
  • Thresholding method, K means clustering, Manual analysis.
  • The outsourcing decryption technique and a newly proposed key private proxy re-encryption are adapted.


  • No security for users? data. No authentication or security provided
  • High resource costs are needed for the implementation.
  • Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification.

Proposed system:

  • In this paper, we proposed a classification algorithm, naive Bayes, and Decision tree algorithm s are used.
  • In the Proposed System, we use two different types of algorithms. For Finding the diseases such as Heart and Lung and Diabetes


  • High accuracy, fastest prediction, and consistency of results.
  • It can segment the lung, heart, and diabetes regions from the data accurately.

It is useful to classify the lung Tumor from trained data set for accurate detection.

Early Disease Prediction using AI
Early Disease Prediction using AI


Hardware and Software Requirements:


  1. Windows 7,8,10 64 bit
  2. RAM 4GB


  1. Python 2.7
  2. Anaconda Navigator

Python?s standard library

  • Pandas
  • Numpy
  • Sklearn
  • tkMessageBox
  • matplotlib

Customer Reviews

There are no reviews yet.

Be the first to review “Early Disease Perdition using Machine Learning”

This site uses Akismet to reduce spam. Learn how your comment data is processed.