Disease Prediction Using Machine Learning

Big data has a major impact on healthcare analytics and it have the capacity to
reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and
improve the quality of life. Accurate analysis of medical data benefits in early disease detection
and well patient care in big data. The analysis accuracy is reduced when we have incomplete
data. In this paper, a machine learning algorithm is used for effective prediction of diseases.
Latent factor model is used to overcome the difficulty of missing data.
Moreover, different regions exhibit unique characteristics of certain regional disease-
frequent communities.
One such implementation of machine learning algorithms is in the field of
healthcare.Medical facilities need to be advanced so that better decisions for patient diagnosis
and treatment options can be made. Machine learning in healthcare aids the humans to process
huge and complex medical datasets and then analyze them into clinical insights.This then can
further be used by physicians in providing medical care.
Hence machine learning when implemented in healthcare can leads to increased patient
satisfaction. In this paper,we try to implement functionalities of machine learning in healthcare
in a single system.Instead of diagnosis, when a disease prediction is implemented using certain
machine learning predictive algorithms then healthcare can be made smart. Some cases can occur
when early diagnosis of a disease is not within reach.
Hence disease prediction can be effectively implemented. As widely said “Prevention is
better than cure”,predicition of disease.This paper mainly focuses on the development of a
system or we could say an immediate medical provision which would incorporate the symptoms
collected from multisensory devices and other medical data and store them into a healthcare
dataset.The Machine learning algorithms that we use on this dataset are Decision Tree
algorithm, Random Forest algorithm,Naïve Bayes algorithm