Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Factors facilitating a national quality registry to aid clinical quality improvement : findings of a national survey

Objectives: While national quality registries (NQRs) are suggested to provide opportunities for systematic follow-up and learning opportunities, and thus clinical improvements, features in registries and contexts triggering such processes are not fully known. This study focuses on one of the world's largest stroke registries, the Swedish NQR Riksstroke, investigating what aspects of the registry and healthcare organisations facilitate or hinder the use of registry data in clinical quality improvement. Methods: Following particular qualitative studies, we performed a quantitative survey in an exploratory sequential design. The survey, including 50 items on context, processes and the registry, was sent to managers, physicians and nurses engaged in Riksstroke in all 72 Swedish stroke units. Altogether, 242 individuals were presented with the survey; 163 responded, representing all but two units. Data were analysed descriptively and through multiple linear regression. Results: A majority (88%) considered Riksstroke data to facilitate detection of stroke care improvement needs and acknowledged that their data motivated quality improvements (78%). The use of Riksstroke for quality improvement initiatives was associated (R2=0.76) with ‘Colleagues’ call for local results’ (p=<0.001), ‘Management Request of Registry data’ (p=<0.001), and it was said to be ‘Simple to explain the results to colleagues’ (p=0.02). Using stepwise regression, ‘Colleagues’ call for local results’ was identified as the most influential factor. Yet, while 73% reported that managers request registry data, only 39% reported that their colleagues call for the unit's Riksstroke results. Conclusions: While an NQR like Riksstroke demonstrates improvement needs and motivates stakeholders to make progress, local stroke care staff and managers need to engage to keep the momentum going in terms of applying registry data when planning, performing and evaluating quality initiatives.