12,004 research outputs found
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study
Mobility and network traffic have been traditionally studied separately.
Their interaction is vital for generations of future mobile services and
effective caching, but has not been studied in depth with real-world big data.
In this paper, we characterize mobility encounters and study the correlation
between encounters and web traffic profiles using large-scale datasets (30TB in
size) of WiFi and NetFlow traces. The analysis quantifies these correlations
for the first time, across spatio-temporal dimensions, for device types grouped
into on-the-go Flutes and sit-to-use Cellos. The results consistently show a
clear relation between mobility encounters and traffic across different
buildings over multiple days, with encountered pairs showing higher traffic
similarity than non-encountered pairs, and long encounters being associated
with the highest similarity. We also investigate the feasibility of learning
encounters through web traffic profiles, with implications for dissemination
protocols, and contact tracing. This provides a compelling case to integrate
both mobility and web traffic dimensions in future models, not only at an
individual level, but also at pairwise and collective levels. We have released
samples of code and data used in this study on GitHub, to support
reproducibility and encourage further research
(https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3
adds GitHub lin
Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results
The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules)-i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly-used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines and the cAnt-MinerPB producing ordered rules are also presented
Mining heterogeneous information graph for health status classification
In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting peopleâs health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patientsâ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patientsâhealth status and reveal the specific disease potentially suffered
by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of peopleâs health with
accessibility to the patterns in various observations
Granular computing based approach of rule learning for binary classification
Rule learning is one of the most popular types of machine-learning approaches, which typically follow two main strategies: âdivide and conquerâ and âseparate and conquerâ. The former strategy is aimed at induction of rules in the form of a decision tree, whereas the latter one is aimed at direct induction of ifâthen rules. Due to the case that the divide and conquer strategy could result in the replicated sub-tree problem, which not only leads to overfitting but also increases the computational complexity in classifying unseen instances, researchers have thus been motivated to develop rule learning approaches through the separate and conquer strategy. In this paper, we focus on investigation of the Prism algorithm, since it is a representative one that follows the separate and conquer strategy, and is aimed at learning a set of rules for each class in the setting of granular computing, where each class (referred to as target class) is viewed as a granule. The Prism algorithm shows highly comparable performance to the most popular algorithms, such as ID3 and C4.5, which follow the divide and conquer strategy. However, due to the need to learn a rule set for each class, Prism usually produces very complex rule-based classifiers. In real applications, there are many problems that involve one target class only, so it is not necessary to learn a rule set for each class, i.e., only a set of rules for the target class needs to be learned and a default rule is used to indicate the case of non-target classes. To address the above issues of Prism, we propose a new version of the algorithm referred to as PrismSTC, where âSTCâ stands for âsingle target classâ. Our experimental results show that PrismSTC leads to production of simpler rule-based classifiers without loss of accuracy in comparison with Prism. PrismSTC also demonstrates sufficiently good performance comparing with C4.5
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