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Melanoma is the fastest growing cancer worldwide, and 1 in 50 Americans will develop it in their lifetime. Sentinel lymph node (SLN) metastasis is one of the most important prognostic indicators for melanoma survival. We present several machine learning models for predicting SLN metastasis using data from a real-world dermatology electronic health record (EHR) system. The class label is the result...
The detection of phishing websites using traditional machine learning methods has been demonstrated in previous studies. Traditional machine learning methods assume that the input feature space is the same between the training and testing data. There are scenarios in machine learning, where the available labeled training data has a different input feature space than the testing data. In cases where...
Outlier detection is a critical function across a diverse range of tasks and domains. There are numerous outlier detection methods, the majority of which produce scores to indicate an outlier versus inlier. An issue with these scores is that they can be difficult to interpret and do not allow for comparisons between different methods. One solution is to convert the outlier score to probabilities....
Injuries in professional soccer games are very common and can greatly impact players, teams, and leagues. The ability to predict conditions under which injuries are likely to occur would help to mitigate competitive and financial losses. This paper presents a case study in which we look at injuries during 713 Major League Soccer games spanning the 2015 and 2016 seasons. Our dataset consists of 713...
Distributed Denial of Service (DDoS) attacks are a popular and inexpensive form of cyber attacks. Application layer DDoS attacks utilize legitimate application layer requests to overwhelm a web server. These attacks are a major threat to Internet applications and web services. The main goal of these attacks is to make the services unavailable to legitimate users by overwhelming the resources on a...
Fraud, waste, and abuse in medical insurance contributes to significant increases in costs for providers and patients. One way to reduce costs is through the detection of abnormal medical practices that could indicate possible fraud. In this paper, we expand upon our previous research into medical specialty anomaly detection by validating the efficacy of our model using real-world fraud cases, and...
We present the Modernizing Analytics for MELanoma (MAMEL) dataset: a real-world, dermatologyspecific research dataset specifically crafted to advance data mining and machine learning research in the field of melanoma diagnosis, analysis, and treatment. This dataset was collected and curated from Modernizing Medicine’s EMA DermatologyTM application, a cloud-based Electronic Health Record (EHR) platform...
We propose and demonstrate an approach for the often attempted problem of market prediction. We restrict our study to a widely purchased and well recognized commodity, crude oil, which experiences significant volatility. Robust debate exists over the applicability of the weak and semi-strong versions of the Efficient Market Hypothesis (EMH) to financial markets. In this paper we train nine learners...
In machine learning applications, there are scenarios of having no labeled training data, due to the data being rare or too expensive to obtain. In these cases, it is desirable to use readily available labeled data, that is similar to, but not the same as, the domain application of interest. Transfer learning algorithms are used to build high-performance classifiers, when the training data has different...
Face recognition methods are evaluated against face image databases. Recent face image databases provide an evaluation protocol for an impartial comparison and assessment of where a facial recognition algorithm stands compared to other methods. Unfortunately, many authors test their facial recognition methods using either restricted face databases, random subsets from public databases, or do not follow...
Healthcare is an integral component in people's lives, especially for the rising elderly population. Medicare is one such healthcare program that provides for the needs of the elderly. It is imperative that these healthcare programs are affordable, but this is not always the case. Out of the many possible factors for the rising cost of healthcare, claims fraud is a major contributor, but its impact...
Whether purchasing a product or searching for a new doctor, consumers often turn to online reviews for recommendations. Determining whether reviews are truthful is imperative to the consumer, as to not get misled by false recommendations. Unfortunately, it is often difficult, or impossible, for humans to ascertain the validity of a review through reading the text, however, studies have shown machine...
A transfer learning environment is characterized by a machine learning algorithm being trained with data from one domain (the source domain) and being tested on data from a different domain (the target domain). In a transfer learning scenario, the class probability of the source domain may be different from the class probability of the target domain, which is referred to as "domain class imbalance"...
Previous research focusing on the evaluation of transfer learning algorithms has predominantly used real-world datasets to measure an algorithm's performance. A test with a real-world dataset exposes an algorithm to a single instance of distribution difference between the training (source) and test (target) datasets. These previous works have not measured performance over a wide-range of source and...
Understanding the sentiment conveyed by a person is an important part of any social interaction, and sentiment in text can provide valuable insight into an author's opinion. Sentiment analysis for text is a large field of research within machine learning, as it allows the sentiment of large numbers of text instances to be determined and used to answer various questions, such as election prediction...
The healthcare industry is a complex system with many moving parts. One issue in this field is the misuse of medical insurance systems, such as Medicare. In this paper, we build a machine learning model to detect when physicians exhibit anomalous behavior in their medical insurance claims. This new research has the potential to give some insight in determining if, and when, physicians are acting outside...
Healthcare has and continues to be an integral component in people's lives, especially for the rising elderly population. One such healthcare program that provides for the needs of the elderly is Medicare. It is important that any such program be affordable but, unfortunately, this is not always the case. Out of the many possible factors for the rising cost of healthcare, fraud is a major contributor,...
Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human...
While cancer treatments are constantly advancing, there is still a real risk of relapse after potentially curative treatments. At the risk of adverse side effects, certain adjuvant treatments can be given to patients that are at high risk of recurrence. The challenge, however, is in finding the best tradeoff between these two extremes. Patients that are given more potent treatments, such as chemotherapy,...
Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for...
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