Anomaly Detection Methodologies Research Proposal

Anomaly Detection Methodologies - Research Proposal ExampleBesides, current practices and procedures aimed at identifying much(prenominal) patients are slow, expensive and unsuitable for incorporating new analytical mechanisms. Buckeridge (2007) argues that Current algorithms used for achieving this risk stratification are pendant on the labelling of the patient data as positive or negative. This classification implies that determining trends and subsets that are idealistic in a given population requires an analysis of large data sets and the identification of positive aspects up to a threshold level. This process, as explained above, is not just slow or expensive, but puts additional lading on patients and hospital administrators, thereby affecting the validity and effectiveness of such practices. The proposed study aims to use allot anomalousness perception methods that are known to be suitable for detecting interesting or queer patterns in a given data set. Bohmer (2009) s ays that new frameworks allow anomaly detection to be use towards determining anomalous patterns in subsets of attributes associated with a data set. In simpler words, anomaly detection methods identify droll occurrences with the data that appear to deviate from the normal behavior exhibited by a majority of the data set. Examples of such anomalies include an epidemic outbreak, traffic congestion in a certain section of roads or an attack on a network (Applegate, 2009). The proposed research aims to extend the standard approach to anomaly detection by devising techniques to identify per centumial patterns that exhibit anomalous behaviour with the remainder of the data set. such techniques are believed to aid in the detection and assessment of unusual outcomes or decisions related to patient counseling in healthcare institutions. Anomaly Detection Several studies by researchers like Nurcan (2009) and Anderson (2007) have applied anomaly detection techniques to healthcare. In fac t, anomaly detection has proved useful in areas under clinical behaviour and medical technology such as blood samples, vestibular information, mammograms and electroencephalographic signals (Brandt, 2007). However, the same principles have run aground little application in enhancing the quality of patient care or identifying existing deficiencies in the assist extended to patients. The proposed study aims to improve and extend anomaly detection techniques to such relatively unexplored domains. plot previous studies have relied primarily on detecting existing conditions such as diseases, the proposed research allow for apply similar methods to ascertain the level of risk that accompanies a potential outcome being analyzed. Thus, the step of this risk as a result of uncovering anomalies is likely to help in forecasting the picture of patients to certain diseases or deficiencies. The study proposed to utilize several anomaly detection methods by applying them to existing clinical data on patients. In doing so, the number of outcomes and patients being analyzed will be much bigger and wider than those adopted by previous studies. Some of the detection methods that will be included as part of the proposed study are listed below Nearest Neighbour method As the name suggests, the hot neighbour method helps detect patients (anomalies) from a given population based on information pertaining to their n nearest neighbours. This method is based on the principle of vectors that are used to sum the distances between a set and it n closes neighbours. As a result, dense and sparse regions are identified based on the numerate score which is lesser in the former case