K-Means Clustering Optimization using the Elbow Method and Early Centroid Determination Based-on Mean and Median

Umargono, Edy; Suseno, Jadmiko Endro; Gunawan, S K Vincensius

doi:10.5220/0009908402340240

“…We then performed a hierarchical clustering on the t-SNE output using Euclidean distance. The number of clinically relevant subphenotypes was chosen by combining two clustering quality metrics, namely the Silhouette Coefficient and Elbow Coefficient 9,10 , and validated with an exploratory analysis.…”

Section: Methodsmentioning

confidence: 99%

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Zhou

¹

,

Romero-García

²

,

Badenes

³

et al. 2022

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0

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Background Elderly patients with COVID-19 are among the most numerous populations being admitted in the ICU due to its high mortality rate and high comorbidity incidence. An early severity risk stratification at hospital admission could help optimize ICU usage towards those more vulnerable and critically ill patients. Methods Of 503 Spanish patients aged>64 years admitted in the ICU between 26 Feb and 02 Nov 2020 in two Spanish hospitals, we included 193 quality-controlled patients. The subphenotyping combined PCA and t-SNE dimensionality reduction methods to maximize non-linear correlation and reduce noise among age and full blood count tests (FBC) at hospital admission, followed by hierarchical clustering. Findings We identified five subphenotypes (Eld-ICU-COV19 clusters) with heterogeneous FBC patterns associated to significantly disparate 30-day ICU mortality rates ranging from 2% in a healthy cluster to 44% in a severe cluster, along three moderate clusters. Interpretations To our knowledge, this is the first study using age and FBC at hospital admission to early stratify the risk of death in ICU at 30 days in elderly patients. Our results provide guidance to comprehend the phenotypic classification and disparate severity patterns among elderly ICU patients with COVID-19, based only on age and FBC, that have the potential to establish target groups for early risk stratification or early triage systems to provide personalized treatments or aid the decision-making during resource allocation process for each target Eld-ICU-COV19 cluster, especially in those circumstances with resource scarcity problem. Funding FONDO SUPERA COVID-19 by CRUE-Santander Bank grant SUBCOVERWD-19. Research in context Evidence before this study We searched on PubMed and Google Scholar using the search terms “COVID-19”, “SARS-CoV2”, “phenotypes” for research published between 2020 to 2022, with no language restriction, to detect any published study identifying and characterizing phenotypes among ICU COVID-19 patients. A previous COVID-19 phenotyping study found three phenotypes from hospitalized patients associated with significantly disparate 30-day mortality rates (ranging from 2·5 to 60·7%). However, it seems to become harder to find phenotypes with discriminative mortality rates among ICU COVID-19 patients. For example, we found one study that uncovered two phenotypes from 39 ICU COVID-19 patients based on biomarkers with 39% and 63% mortality rates, but such difference was not statistically significant. We also found another study with more success that uncovered two ICU COVID-19 phenotypes using two different trajectories with somehow disparate 28-day mortality rates of 27% versus 37% (Ventilatory ratio trajectories) and of 25% versus 39% (mechanical power trajectories). Added value of this study To our knowledge, this is the first study that uses age and laboratory results at hospital admission (i.e., before ICU admission) in elderly patients to early stratify, prior ICU admission, the risk of death in ICU at 30 days. We classified 193 patients with COVID-19, based on age and ten Full Blood Count (FBC) tests, into five subphenotypes (one healthy, three moderate, and one severe) that showed significantly disparate 30-day ICU mortality rates from 2% to 44%. Implications of all the available evidence Identifying, from elderly ICU patients with COVID-19 (Eld-ICU-COV19), subphenotypes could spur further investigation to analyze the potential differences in their underlying disease mechanisms, acquire better phenotypical understanding among Eld-ICU-COV19 toward better decision-making in distributing the limited resources (including both logistic and medical) as well as shedding light on tailoring personalized treatment for each specific target subgroup in future medical research and clinical trial.

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“…This method compares the difference in the sum of squared errors (SSE) of each cluster. The elbow angle's most extreme difference shows the best cluster number [25]. Evaluation of the best cluster number for the k-means is illustrated in Figure 12.…”

Section: Comparison Of Clustering Methodsmentioning

confidence: 99%

Buried Wire Detection and Clustering Algorithm with Ground Penetrating Radar

AKBULUT

¹

,

Pakfiliz

²

2022

76

4

0

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Improvised explosive devices (IEDs) have recently become a considerable national security concern for governments due to their increased use. Since IEDs do not have a particular shape, detecting and classifying IEDs becomes a complex problem. Due to the irregularity of the wire shape, recent research focuses on detecting command wire, which is the triggering mechanism in IEDs. This study proposes a detection and clustering algorithm for wire detection. The synthetic data were generated using the gprMax software, an open-source Finite-Difference Time-Domain (FDTD) simulation environment. Different wire and clutter orientations were simulated while creating a 2D GPR database. Three different prescreening algorithms are compared concerning computational time and signal-to-noise ratio. The Go Decomposition (GoDec) method was used at the preprocessing stage. Discriminating the buried wire from clutter was conducted using the k-means clustering method. The proposed algorithm results show promising outcomes over simulated GPR 2D C-scans.

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“…where is the number of geological type clusters, is the set of points in i -th cluster, is the centroid of the cluster , and is a vector of geotechnical parameters in the cluster Because the number of clusters cannot be determined directly, we use the elbow method 30 to obtain the optimal number of clusters by calculating the values for various cluster numbers. The elbow method assumes that the relationship graph of and is in the shape of an 'elbow,' and the point of inflection on the curve is the optimal number of clusters.…”

Section: Geological Type Redefinitionmentioning

confidence: 99%

A Multi-stage Model for Rapid Identification of Geological Features in Shield Tunnelling

Hou

¹

,

Lu

²

,

Zhou

³

et al. 2022

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0

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Decision-making on shield construction parameters depends on timely and accurate geological properties feedback. Real-time mastering of geological status around the shield during tunneling is necessary to achieve safe and efficient construction. However, balancing the model generalizability and the accuracy of geological identification remains a significant challenge. This paper proposes a Rapidly Geological Features Identification (RGFI) method that predicts the percentage of each stratum in the excavation face under different geological environments. The method consists of three stages: k-means-based geological type redefinition, eXtreme Gradient Boosting based geological type classification and convolutional neural network stratum distribution prediction with an attention mechanism. The method produces good generalization performance and achieves higher accuracy when simulating tunnel construction data for self-driving shield in the interval between Qian-Zhuang and Ke-Ning Road of Line 5 of Nanjing Metro in China. Furthermore, the method successfully provided more accurate geological information for the Hangzhou-Shaoxing intercity railroad tunnel project. It helped the ‘ZhiYu’ shield to adjust the construction parameters quickly and improve the safety and quality of the project.

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K-Means Clustering Optimization using the Elbow Method and Early Centroid Determination Based-on Mean and Median

Cited by 37 publications

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Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Buried Wire Detection and Clustering Algorithm with Ground Penetrating Radar

A Multi-stage Model for Rapid Identification of Geological Features in Shield Tunnelling

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