An Innovative ‘Cluster-then-Predict’ Approach for Improved Sentiment Prediction

Soni, Rishabh; Mathai, K. James

doi:10.1007/978-981-10-1023-1_13

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Based on the important features identified, we adopt the cluster-then-predict approach to build the final AI models [22], [23]. Specifically, we first cluster the patients into subgroups, and then train a decision tree classifier for each subgroup of patients.…”

Section: The Cluster-then-predict Approachmentioning

confidence: 99%

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF Sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Leong¹,

Xiao²,

Yun³

et al. 2023

IEEE Access

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Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients' volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A "cluster-then-predict" approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed "Chest Patch" RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases. INDEX TERMSBiomedical measurements, Biomedical monitoring, Mobile measurements, RF sensors, Artificial intelligence, Machine learning I. INTRODUCTION C HRONIC heart failure, pulmonary hypertension, acute respiratory disorder syndrome (ARDS), coronavirus disease (COVID), kidney failure, and their acute exacerbation are among the leading causes of hospitalization, health care costs, and deaths in the United States and around the

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Section: The Cluster-then-predict Approachmentioning

confidence: 99%

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF Sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Leong¹,

Xiao²,

Yun³

et al. 2023

IEEE Access

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“…The cluster-then-predict models employed in this study can be separated into three different semi-supervised and independent classification models. Unsupervised methods are used to find clusters in the dataset, and a supervised model is subsequently used to assign labels to the cluster (Soni and Mathai, 2015;Trivedi et al, 2015). As an unsupervised model, k-means clustering (Forgy, 1965;Lloyd, 1982), mixture model clustering (GMM) (Bishop, 2006), and Bayesian Gaussian mixture models (BGMM) (Bishop, 2006) were used.…”

Section: Modelsmentioning

confidence: 99%

Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms

Kaltenborn,

Macfarlane,

Clay

et al. 2023

Geosci. Model Dev.

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Abstract. Snow-layer segmentation and classification are essential diagnostic tasks for various cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimeter intervals in snow depth. The resulting depth–force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is, in theory, possible but cannot be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. We evaluate how well machine learning models can automatically segment and classify SMP profiles to address this gap. We trained 14 models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, an extensive collection of snow profiles on Arctic sea ice. SMP profiles can be successfully segmented and classified into snow classes based solely on the SMP's signal. The model comparison provided in this study enables SMP users to choose a suitable model for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Anyone can access the tools and models needed to automate snow type identification via the software repository “snowdragon”. Overall, snowdragon creates a link between traditional snow classification and high-resolution force–depth profiles. Traditional snow profile observations can be compared to SMP profiles with such a tool.

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“…It is considered to be relatively computational efficient. In (Soni and Mathai, 2015), a 'cluster-then-predict' model was proposed to improve the accuracy of predicting Twitter sentiment through a composition of both supervised and unsupervised learning. After building the dataset, k-Means was performed such that tweets with similar words are clustered together.…”

Section: Hard Clusteringmentioning

confidence: 99%

Cluster Analysis of Twitter Data: A Review of Algorithms

Alnajran

Crockett

McLean

et al. 2017

Proceedings of the 9th International Conference on Agents and Artificial Intelligence

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Twitter, a microblogging online social network (OSN), has quickly gained prominence as it provides people with the opportunity to communicate and share posts and topics. Tremendous value lies in automated analysing and reasoning about such data in order to derive meaningful insights, which carries potential opportunities for businesses, users, and consumers. However, the sheer volume, noise, and dynamism of Twitter, imposes challenges that hinder the efficacy of observing clusters with high intra-cluster (i.e. minimum variance) and low inter-cluster similarities. This review focuses on research that has used various clustering algorithms to analyse Twitter data streams and identify hidden patterns in tweets where text is highly unstructured. This paper performs a comparative analysis on approaches of unsupervised learning in order to determine whether empirical findings support the enhancement of decision support and pattern recognition applications. A review of the literature identified 13 studies that implemented different clustering methods. A comparison including clustering methods, algorithms, number of clusters, dataset(s) size, distance measure, clustering features, evaluation methods, and results was conducted. The conclusion reports that the use of unsupervised learning in mining social media data has several weaknesses. Success criteria and future directions for research and practice to the research community are discussed.

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An Innovative ‘Cluster-then-Predict’ Approach for Improved Sentiment Prediction

Cited by 8 publications

References 4 publications

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF Sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF Sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms

Cluster Analysis of Twitter Data: A Review of Algorithms

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