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Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently, diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a...
With the development of the aviation industry and the improvement of people's living standard, more and more people choose aircraft as their way of travel, but the airline adjusts the price according to the revenue management in real time. The purpose of this paper is to design different decision-making tools from the customer's perspective, and to provide customers with the relevant information needed...
This paper proposes a novel ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and an application of adaptive selection of the most suitable model for each data instance. Ensemble methods, an important type of machine learning technique, have drawn a lot of attention in both academic research and practical applications,...
Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew members. The fateful incident still compel the researchers and analysts to understand what can have led to the survival of some passengers and demise of the others. With the use of machine learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set, the research...
In today's digital age, availability of health related information are not only limited to traditional sources like Clinical data, Claims Data, NIS (National Inpatient Sample) and EMRs but they are also being made available from digital sources like Smart Watch, Health Trackers, Glucose Meters, Blood Pressure Monitors and many other newer personal electronics devices. Patients are also often sharing...
The idea behind this tool is to make a generic classifier which can be used to diagnose patients suffering from brain disorders. We have created a tool which uses machine learning algorithms from Weka, Caret and Scikit Learn from Java, R and Python respectively and combines the three packages into one R package which provides the functionality of classifying the patients suffering from brain disorders...
We investigate the performance of complex trading rules in equity price direction prediction, over and above continuous-valued indicators and simple technical trading rules. Ten of the most popular technical analysis indicators are included in this research. We use Random Forest ensemble classifiers using minute-by-minute stock market data. Results show that our models have predictive power and yield...
A concept of Four Properties (SiQi) of Chinese herbs is the important part of traditional Chinese medicine theory. The Chinese clinical medicine is a process of dialectical theory of governance of Chinese medicine prescriptions based these four properties. The Chinese medicine prescription uses a "Cold" and "Hot" model to judge the properties of Chinese herbs, and also judge the...
Occurrence of multiple seizures is a common phenomenon observed in patients with epilepsy: a neurological malfunction that affects approximately 50 million people in the world. Seizure prediction is widely acknowledged as an important problem in the neurological domain, as it holds promise to improve the quality of life for patients with epilepsy. A noticeable number of clinical studies showed evidence...
Internet and various services offered by it has become a daily routine. The Quality of Web Service (QWS) has become a significant factor in distinguishing the success of service providers. The main purpose of this paper is to analyze quality prediction using the IKS hybrid model with a new approach of data classification. We present the IKS hybrid model. The model combines selection of features, clustering...
The difficulty of understanding a financial institution's risk of default has been highlighted by multiple recent episodes in both the U.S. and in Europe. This paper describes a study on the empirical comparison of classification techniques for predictive ranking of the 12 month risk of default in banks. This work compares the scoring capabilities of different predictive models. The models compared...
A new procedure for combined validation of learning models - developed for specifically uncertain data - is briefly described; it relies on a combination of resubstitution with the modified learn-and-test paradigm, called by us the queue validation. In the initial experiment the elaborated procedure was checked on doubtful (presumably distorted by creative accounting) data, related to small and medium...
Machine Learning algorithms are difficult to directly apply among data sets of high dimensionality. This paper examines application of hybrid algorithms to segment data models to enable a higher level of accuracy. Our process begins with the reduction of our input parameter sets through the derivation of dominant characteristics. Using these characteristics, ranges are determined in which to segment...
There are many unimportant features in the hypertension sample data set in the three gorges area, which are gathered by Tongji medical college, school of HUST. These redundant irrelevant features spoil the classification, increase many unwanted calculations and decrease the real-time capacity of the medical prediction. In order to solve above problem, an improved hypertension prediction model based...
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