scholarly journals An Improved Heart Disease Classification System Using Probabilistic Principal Component Analysis and K-Nearest Neighborhood

2018 ◽  
Vol 4 (3) ◽  
pp. 45-52
Author(s):  
Akinyemi Omololu Akinrotimi ◽  
Dayo Reuben Aremu
Keyword(s):  
Principal Component Analysis ◽  
Heart Disease ◽  
Classification System ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Classification ◽  
Probabilistic Principal Component Analysis ◽  
Nearest Neighborhood

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

2017 ◽  
Vol 482 ◽  
pp. 796-807 ◽  
Author(s):  
Syed Muhammad Saqlain Shah ◽  
Safeera Batool ◽  
Imran Khan ◽  
Muhammad Usman Ashraf ◽  
Syed Hussnain Abbas ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Heart Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis ◽  
Probabilistic Principal Component Analysis

scholarly journals Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dibo Hou ◽  
Shu Liu ◽  
Jian Zhang ◽  
Fang Chen ◽  
Pingjie Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Contamination ◽  
Distribution Systems ◽  
Water Distribution ◽  
Online Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Probabilistic Principal Component Analysis ◽  
Contamination Events

This study proposes a probabilistic principal component analysis- (PPCA-) based method for online monitoring of water-quality contaminant events by UV-Vis (ultraviolet-visible) spectroscopy. The purpose of this method is to achieve fast and sound protection against accidental and intentional contaminate injection into the water distribution system. The method is achieved first by properly imposing a sliding window onto simultaneously updated online monitoring data collected by the automated spectrometer. The PPCA algorithm is then executed to simplify the large amount of spectrum data while maintaining the necessary spectral information to the largest extent. Finally, a monitoring chart extensively employed in fault diagnosis field methods is used here to search for potential anomaly events and to determine whether the current water-quality is normal or abnormal. A small-scale water-pipe distribution network is tested to detect water contamination events. The tests demonstrate that the PPCA-based online monitoring model can achieve satisfactory results under the ROC curve, which denotes a low false alarm rate and high probability of detecting water contamination events.

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