Labeling Methods for Identifying Outliers

IJSER Publication, 2013

An outlier is an observations which deviates or far away from the rest of data. There are two kinds of outlier methods, tests discordance and labeling methods. In this paper, we have considered the medical diagnosis data set finding outlier with discordancy test and comparing the performance of outlier detection. Most of the outlier detection methods considered as extreme value is an outlier. In some cases of outlier detection methods no need to use statistical table. The suggested outlier detection methods using the context of detection sensitivity and difficulties of analyzing performance for outlier detections are compared.

International Journal of Engineering Science, 2012

Outlier detection in the medical and public health domains typically works with patient records and is a very critical problem. This paper elaborates how the outliers can be detected by using statistical methods. A total of 78, 67, 82, 78 and 69 outliers in five medical datasets are detected for the statistics namely leverage, R-standard, R-student, DFFITS, Cook's D and covariance ratio. The results of the present investigation suggest that (i) the extraordinary behavior of outliers facilitates the exploration of the valuable knowledge hidden in their domain and help the decision makers to provide improved, reliable and efficient healthcare services (ii) medical doctors can use the present experimental results as a tool to make sensible predictions of the vast medical databases and finally (iii)a thorough understanding of the complex relationships that appear with regard to patient symptoms, diagnoses and behavior is the most promising area of outlier mining.

IJAR - Indian Journal of Applied Research, 2020

Background: Patient medical records contain many entries relating to patient conditions, treatments and lab results. Generally involve multiple types of data and produces a large amount of information. These databases can provide important information for clinical decision and to support the management of the hospital. Medical databases have some specificities not often found in others non-medical databases. In this context, outlier detection techniques can be used to detect abnormal patterns in health records (for instance, problems in data quality) and this contributing to better data and better knowledge in the process of decision making. Aim: This systematic review intention to provide a better comprehension about the techniques used to detect outliers in healthcare data, for creates automatisms for those methods in the order to facilitate the access to information with quality in healthcare.

Indonesian Journal of Electrical Engineering and Computer Science

The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlie...

International Journal of Computer Applications, 2013

Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. This paper has surveyed upon data mining and its various techniques that are used to extract useful information such as clustering, and has also surveyed the techniques that are used to detect the outliers. This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user.

— Outlier is defined as an event that deviates too much from other events. The identification of outlier can lead to the discovery of useful and meaningful knowledge. Outlier means it's happen at some time it's not regular activity. Research about Detection of Outlier has been extensively studies in the past decade. However, most existing research focused on the algorithm based on specific knowledge, compared with outlier detection approach is still rare. In this paper mainly focused on different kind of outlier detection approaches and compares it's prone and cones. In this paper we mainly distribute of outlier detection approach in two parts classic outlier approach and spatial outlier approach. The classical outlier approach identifies outlier in real transaction dataset, which can be grouped into statistical approach, distance approach, deviation approach, and density approach. The spatial outlier approach detect outlier based on spatial dataset are different from transaction data, which can be categorized into spaced approach and graph approach. Finally, the comparison of outlier detection approaches.

Outliers is view as an error data in information which is turned into important crisis that has been investigated in various areas of study plus functional fields. Several outlier detection methods have been implemented to assured functional fields, whereas several methods are supplementary basic. Various functional areas are also investigated in severe privacy like study on offense as well as terrorist behaviors. Through the improvement in information skills, the numeral of records, plus their measurement as well as difficulty, raise fast, that outcome in the need of computerized examination of huge quantity of various ordered data. For this intention, different data mining systems are utilized. The objective of these types of systems is to detect unseen dependencies from the records. Outlier detection in data mining is the detection of objects, remarks or observations that doesn't match to a predictable sample in a set of record. This detection technique is more beneficial in the several areas such as health trade, offense finding, fake operation, community protection and so on. In this paper we have studied different outlier detection algorithms such as Cluster based outlier detection, Distance based outlier detection plus Density based outlier detection. Result experimentation is done on different four dataset to identify the outliers and the comparative result shows that the cluster based methods are efficient for calculation of clusters and density-based outlier detection algorithm offers improved accuracy and faster execution for identification of outliers than other two outlier detection algorithm.

International Journal of Engineering Sciences and Research Technology, 2016

Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the monstrous data sets. Outlier detection is one of the imperative parts of data mining which Rexall discovers the perceptions that are going amiss from the normal expected conduct. Outlier detection and investigation is once in a while known as Outlier mining. In this paper, we have attempted to give the expansive and a far reaching literature survey of Outliers and Outlier detection procedures under one rooftop, to clarify the lavishness and multifaceted nature connected with each Outlier detection technique. Besides, we have likewise given a wide correlation of the different strategies for the diverse Outlier techniques. Outliers are the focuses which are unique in relation to or conflicting with whatever is left of the information. They can be novel, new, irregular, strange or uproarious data. Outliers are in some cases more fascinating than most of the information. The principle di...

The outlier detection problem has important applications in the field of medical research. Clinical databases have accumulated large quantities of information about patients and their medical conditions. In this study, the data mining techniques are used to search for relationships in a large clinical database. Relationships and patterns within this data could provide new medical knowledge. The main objective of this paper is to detect the outliers and identify the influence factor in the diabetes symptoms of the patient using data mining techniques. Results are illustrated numerically and graphically.