Multiple Disease Detection Using Machine Learning
Mrs. Smiley Gandhi*1,Prakhar Bhatt*2,Harsh Srivastava*3,Ayush Pandey*4, Divyansh Barar*5
*1Assistant professor, Department of Computer Science & Engineering, BBDITM, Lucknow, UP, INDIA
*2,3,4,5Student, Department of Computer Science & Engineering, BBDITM, Lucknow, UP, INDIA
ABSTRACT
Because of numerous contributing risk factors, including diabetes, high blood pressure, high cholesterol, irregular pulse rate, and many other factors, it is challenging to diagnose heart disease. The severity of cardiac disease in humans has been determined using various data mining and neural network techniques. According to the research paper the power of the proposed model was quite satisfactory and it was able to predict the evidence of heart disease in a particular individual using KNN and logistic regression which showed good accuracy compared to previously used classifiers like naïve bayes etc.
Healthcare is a very prominent research field with rapid technological advancement and increasing data day by day. In order to deal with a large volume of healthcare data we need Big Data Analytics which is an emerging approach in the Healthcare domain. Millions of patients seek treatments around the globe with various procedures. Making informed and effective decisions to enhance the general standard of healthcare will be aided by analyzing the trends in patient treatment for the diagnosis of a specific condition.
According to the research report, to forecast diabetes mellitus, we have used decision trees, random forests, and neural networks. As given in the paper Pima Indian Diabetes Dataset is also used for the prediction.
Diagnosis of Parkinson disease through a machine learning approach provides better understanding from PD dataset in the present decade. Orange v2.0b and weka v3.4.10 have been used in the present experimentation for the statistical analysis, classification, Evaluation and unsupervised learning methods. The Centre for Machine Learning and Intelligent Systems has retrieved a voice dataset for Parkinson's disease from the UCI Machine learning repository.
