Parkinson’s Brain Disease Prediction Using Big Data Analytics

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N. Shamli 1,* B. Sathiyabhama 1

1. Department of Computer Science and Engineering, Sona College of Technology, Salem, India

* Corresponding author.


Received: 25 Jul. 2015 / Revised: 17 Nov. 2015 / Accepted: 23 Feb. 2016 / Published: 8 Jun. 2016

Index Terms

Big Data, Predictive Analytics, Parkinson's disease, Voice dataset


In healthcare industries, the demand for maintaining large amount of patients' data is steadily growing due to rising population which has resulted in the increase of details about clinical and laboratory tests, imaging, prescription and medication. These data can be called "Big Data", because of their size, complexity and diversity. Big data analytics aims at improving patient care and identifying preventive measures proactively. To save lives and recommend life style changes for a peaceful and healthier life at low costs. The proposed predictive analytics framework is a combination of Decision Tree, Support Vector Machine and Artificial Neural Network which is used to gain insights from patients. Parkinson's disease voice dataset from UCI Machine learning repository is used as input. The experimental results show that early detection of disease will facilitate clinical monitoring of elderly people and increase the chances of their life span and improved lifestyle to lead peaceful life.

Cite This Paper

N. Shamli, B. Sathiyabhama, "Parkinson's Brain Disease Prediction Using Big Data Analytics", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.6, pp.73-84, 2016. DOI:10.5815/ijitcs.2016.06.10


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