A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Data Masking, Encryption, and their Effect on Classification Performance: Trade-offs Between Data Security and Utility

As data mining increasingly shapes organizational decision-making, the quality of its results must be questioned to ensure trust in the technology. Inaccuracies can mislead decision-makers and cause costly mistakes. With more data collected for analytical purposes, privacy is also a major concern. Data security policies and regulations are increasingly put in place to manage risks, but these policies and regulations often employ technologies that substitute and/or suppress sensitive details contained in the data sets being mined. Data masking and substitution and/or data encryption and suppression of sensitive attributes from data sets can limit access to important details. It is believed that the use of data masking and encryption can impact the quality of data mining results. This dissertation investigated and compared the causal effects of data masking and encryption on classification performance as a measure of the quality of knowledge discovery. A review of the literature found a gap in the body of knowledge, indicating that this problem had not been studied before in an experimental setting. The objective of this dissertation was to gain an understanding of the trade-offs between data security and utility in the field of analytics and data mining. The research used a nationally recognized cancer incidence database, to show how masking and encryption of potentially sensitive demographic attributes such as patients’ marital status, race/ethnicity, origin, and year of birth, could have a statistically significant impact on the patients’ predicted survival. Performance parameters measured by four different classifiers delivered sizable variations in the range of 9% to 10% between a control group, where the select attributes were untouched, and two experimental groups where the attributes were substituted or suppressed to simulate the effects of the data protection techniques. In practice, this represented a corroboration of the potential risk involved when basing medical treatment decisions using data mining applications where attributes in the data sets are masked or encrypted for patient privacy and security concerns

Basics of Feature Selection and Statistical Learning for High Energy Physics

This document introduces basics in data preparation, feature selection and learning basics for high energy physics tasks. The emphasis is on feature selection by principal component analysis, information gain and significance measures for features. As examples for basic statistical learning algorithms, the maximum a posteriori and maximum likelihood classifiers are shown. Furthermore, a simple rule based classification as a means for automated cut finding is introduced. Finally two toolboxes for the application of statistical learning techniques are introduced.Comment: 12 pages, 8 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 200

Kidney Ailment Prediction under Data Imbalance

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases. In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related to class imbalance problem. This occurs when at least one of the classes are represented by significantly smaller number of samples than the others in the training set. The problem with imbalanced dataset is that the classifiers tend to classify all samples as majority class, ignoring the minority class samples. The second issue is on the specific type of data to be used for a given problem. Here, we focused on predicting kidney diseases based on patient information extracted from laboratory and questionnaire data. Most recent approaches for predicting kidney diseases or other chronic diseases rely on the usage of prescription drugs. In this study, we focus on biomarker and anthropometry data of patients to analyze and predict kidney-related diseases. In this research, we adopted a learning approach which involves repeated random data sub-sampling to tackle the class imbalance problem. This technique divides the samples into multiple sub-samples, while keeping each training sub-sample completely balanced. We then trained classification models on the balanced data to predict the risk of kidney failure. Further, we developed an intelligent fusion mechanism to combine information from both the biomarker and anthropometry data sets for improved prediction accuracy and stability. Results are included to demonstrate the performance

Document-level sentiment analysis of email data

Sisi Liu investigated machine learning methods for Email document sentiment analysis. She developed a systematic framework that has been qualitatively and quantitatively proved to be effective and efficient in identifying sentiment from massive amount of Email data. Analytical results obtained from the document-level Email sentiment analysis framework are beneficial for better decision making in various business settings