Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson\u27s disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD

A Hierarchical Learning Model for Extracting Public Health Data from Social Media

In decision-making processes, particularly in the healthcare domain, each relevant piece of information is important. This is particularly important when it comes to the health conditions for them there remains a high degree of non-determinism regarding treatment approaches. Online social media are places in which people feel free to share their opinions about numerous topics, including public health issues and how individuals have perceived the efficacy of different types of treatments associated with diseases. social media could represent a secondary source that can be used as a supplement to other data sources. This would allow individuals as well as healthcare providers to gain insight related to public health from different angels. In this study, we construct a hierarchical learning model based on Twitter data that can extract valuable knowledge associated with public health. Back pain was selected for our case study to demonstrate how the proposed model works