B. Sathiyabhama

Work place: Department of Computer Science and Engineering, Sona College of Technology, Salem, India

E-mail: sathiyabhama@sonatech.ac.in

Website:

Research Interests: Bioinformatics, Computer systems and computational processes, Data Mining, Data Structures and Algorithms

Biography

Dr. B. Sathiyabhama received her PhD degree in Computer Science and Engineering from National Institute of Technology, Tiruchirappalli, India. She is currently Professor and Head, Department of Computer Science and Engineering at Sona College of Technology, Salem. Her research interests lie in the areas of Data Mining and Bioinformatics, Big Data Analytics, Health Care Informatics, Algorithm Analysis and Compiler Design and Optimization. She has been a Program and Technical Committee member of several conferences. She was the Chair and invited speaker of several workshops on Data mining and Bioinformatics and technical symposia.  She is a reviewer of several journals and conferences.  She is co-authored a book titled Professional ethics and Human Values. She has published widely in international journals and conferences. She has a professional membership in IEEE, ACM, ISTE, CSI and ISRD (Senior Member). She has received many awards for excellence in teaching and research and development contributions in the College and the best student award (PG level). She is also selected for 2010 Who’s who in the world, conducted by Marquis USA.

Author Articles
Parkinson’s Brain Disease Prediction Using Big Data Analytics

By N. Shamli B. Sathiyabhama

DOI: https://doi.org/10.5815/ijitcs.2016.06.10, Pub. Date: 8 Jun. 2016

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.

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