A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

Jane, Nancy Yesudhas; Nehemiah, Khanna H.; Kannan, A.

doi:10.4338/aci-2015-08-ra-0102

Cited by 11 publications

(4 citation statements)

References 55 publications

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“…The end-user can subscribe to the particular ECG related parameter and visualize it as a graphical format. Jane et al, [38] have proposed temporal rough set induced neuro-fuzzy (TRiNF) mining framework, for handling the complex clinical datasets. This framework implements two functionalities namely 1) Temporal Data Acquisition (TDA) used for missing value assertion and temporal pattern extraction and 2) Temporal classifier, for classifying attributes based on Temporal pattern.…”

Section: Sensor Data and Semantificationmentioning

confidence: 99%

“…The sensed data will be in a tremendous amount which makes the assessment of the patient's health by the end-users a challenging task. Temporal rough set induced neurofuzzy (TRiNF) mining framework [38], is an existing methodology, which efficiently handles the complexities in the time series based clinical dataset in terms of irregular observations and missing values. TRiNF implements two functionalities namely, Temporal Data Acquisition (TDA) and temporal classification.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques

Radhika

Bhuvaneswari

Kalpana³

2021

Wireless Pers Commun

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Things or device under the Internet of Things, when connected to the internet, becomes a product, offers various applications and services for end-users. Existing methodologies offer sensor-based IoT based health-care services, to the end-users populated with lots of sensed values all at a time, making the health-care system with least robustness and inefficient due to unsystematic preview of patient's health record. In order to address this issue, a novel semantic-based service framework is proposed in this paper which allows the end-user to subscribe for a specific physiological parameter, among all the available sensed data, making the health-care system more efficient. In the SeSem framework, Semantification rules and semantification relationship table are applied to the sensed JavaScript object Notation format (JSON) data, in order to semantically separate the JSON data, into a meaningful format. To list the sensed data according to the significance of health issues, a priority is assigned to the sensors in the semantification relationship table. Hence, semantically separated data can be done along with assigned priority. Sensor-based Sematic (SS) ontology is then applied to the semantically separated data, to transform the sensed data more relevant in terms of particular disease and sensor associated with it. The semantically separated sensed data are then published to the Message Queuing Telemetry Transport (MQTT) interface. Using MQTT subscribe, the end-user along with date and time, requests for a particular service, using a Semantic Similarity mapping algorithm, which compares the entire sensed data to that of requested data and responds with a particular physiological parameter request. To make the health care system deploy services in an intelligent way, deep learning algorithm Feedforward Recurrent Neural Networks are applied, which makes the prediction of sensed data based on the latest update when the end-user subscribes for a certain sensed data without specifying date and time. The proposed methodology is evaluated against IoT performance metrics which had shoed showing better results in terms of service-oriented IoT.

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Section: Sensor Data and Semantificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques

Radhika

Bhuvaneswari

Kalpana³

2021

Wireless Pers Commun

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“…Classifying clinical unevenly spaced time series data by imputing missing values has been proposed in [ 22 ]. A framework to classify unevenly spaced time series clinical data using improved double exponential smoothing, rough sets, neural network, and fuzzy logic is proposed in [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Murugesan

Bhuvaneswaran

Nehemiah

et al. 2021

Computational and Mathematical Methods in Medicine

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A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

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“…In most situations, a CMD system cannot be considered to be a gold standard, but it can be used by junior clinicians in the absence of experts to verify and assert their decisions. Computer-assisted systems are used for diagnosis, decision making, and decision support in various medical applications such as cancer care, 10 , 11 heart disease diagnosis, 12 thrombosis diagnosis, 13 diagnosis and treatment of lung disorders, 14 , 15 drug reaction analysis, 16 and allergy diagnosis. 17 …”

Section: Introductionmentioning

confidence: 99%

Computer-assisted Medical Decision-making System for Diagnosis of Urticaria

Christopher

Nehemiah

Kannan

et al. 2016

MDM Policy & Practice

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Background: Urticaria is a common allergic disease that affects all age groups. Allergic disorders are diagnosed at allergy testing centers using skin tests. Though skin tests are the gold standard tests for allergy diagnosis, specialists are required to interpret the observations and test results. Hence, a computer-assisted medical decision-making (CMD) system can be used as an aid for decision support, by junior clinicians, in order to diagnose the presence of urticaria. Methods: The data from intradermal skin test results of 778 patients, who exhibited allergic symptoms, are considered for this study. Based on food habits and the history of a patient, 40 relevant allergens are tested. Allergen extracts are used for skin test. Ten independent runs of 10-fold cross-validation are used to train the system. The performance of the CMD system is evaluated using a set of test samples. The test samples were also presented to the junior clinicians at the allergy testing center to diagnose the presence or absence of urticaria. Results: From a set of 91 features, a subset of 41 relevant features is chosen based on the relevance score of the feature selection algorithm. The Bayes classification approach achieves a classification accuracy of 96.92% over the test samples. The junior clinicians were able to classify the test samples with an average accuracy of 75.68%. Conclusion: A probabilistic classification approach is used for identifying the presence or absence of urticaria based on intradermal skin test results. In the absence of an allergy specialist, the CDM system assists junior clinicians in clinical decision making.

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A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

Abstract: The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

Cited by 11 publications

References 55 publications

An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques

An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Computer-assisted Medical Decision-making System for Diagnosis of Urticaria

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