Parkinson’s Brain Disease Prediction Using Big Data Analytics

Abstract

Abstract-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.

The Impact of Feature Selection Techniques on the Performance of Predicting Parkinson’s Disease

“…5. shows the comparative analysis of the results of Shamli et al [30] with the results of this study. The visualization represents the classifiers and evaluation metrics that were mutual in both of the studies.…”

“…The visualization represents the classifiers and evaluation metrics that were mutual in both of the studies. In Figure 5, we have taken the performance of Decision Tree and SVM from [30] which were performed without any feature selection. On the other hand, we have taken the performance of Random forest and SVM from this study which was trained by selecting top 10 and 15 features respectively.…”

“…Shamli et al [30] proposed a big data based predictive analytics framework with a combination of three different data mining techniques namely: Artificial Neural Network (ANN), Decision tree (DT), and Support Vector Machines (SVM) to gain insights from the tele-monitoring voice dataset of Parkinson's disease affected patients. They have used accuracy, sensitivity, and specificity for evaluating their results.…”

Envisioning Skills for Adopting, Managing, and Implementing Big Data Technology in the 21st Century

“…Similarly, starting in 2011 until today, this firm was able to generate more than ten times as many information per day [4]. (3) Parkinson prediction as highlighted by Shamli and Sathiyabhama in 2016 in their paper Parkinson's Brain Disease Prediction Using Big Data Analytics [5].…”

Big Data Analytics for Medical Applications

“…They can be analyzed to traditional computing techniques. We can produce lots of data [19], e.g., Social Media, Public shipment and GPS. Particularly of data daily we can apply 55 new features, 340 thousand tools and 1 billion records and enter these we can produce 2.5 quintillion bytes per a day, that enough zeros spediculous we know this "Big Data", which is very important.…”