Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

Cheng, Lim Kok; Selamat, Ali; Zabil, Mohd Hazli Mohamed; Selamat, Hafiz; Alias, Rose Alinda; Puteh, Fatimah; Mohamed, Farhan; Krejcar, Ondřej

doi:10.1109/icbda47563.2019.8987044

Cited by 1 publication

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“…This approach identifies pre-clusters or groupings firstly, based on Euclidean (A2) or Log-likelihood distance (A3)-(A5) whether the variables are continuous or categorical, successively constructing a Cluster Features (CF) Tree [71][72][73].…”

Section: Data Availability Statement: Data Available On Request Due To Restrictions (Privacy and Ethical)mentioning

confidence: 99%

“…This approach identifies pre-clusters or groupings firstly, based on Euclidean (A2) or Log-likelihood distance (A3)–(A5) whether the variables are continuous or categorical, successively constructing a Cluster Features (CF) Tree [ 71 , 72 , 73 ].

where B, C are 2 elements from the data input in the Equation (A2), d (m, n) represents the distance between clusters m and n in Equation (A3); <m, n> are the indexes indicating the cluster generated from combining clusters m and n combination; KA-stands for continuous variables total number, while K B -for categorical variables total number; Lp-defines the categories’ number for the p-th categorical variable (A4); Nrpl represents the records number in cluster r whose categorical variable p takes l category; Npl represents the records number in categorical variable p that take the l category;

—represents the estimated variance (dispersion) of the continuous variable p, for the entire dataset;

—defines the estimated variance of the continuous variable p, in cluster n.…”

Section: Appendix A1 In Depth Analysis Of the Subjects’ Recordsmentioning

confidence: 99%

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A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Neamţu

Visa²,

Maniu

et al. 2021

IJERPH

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Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75–100%, intermediate risk 52.9%, and low risk 0–25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy.

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Section: Data Availability Statement: Data Available On Request Due To Restrictions (Privacy and Ethical)mentioning

confidence: 99%

—represents the estimated variance (dispersion) of the continuous variable p, for the entire dataset;

—defines the estimated variance of the continuous variable p, in cluster n.…”

Section: Appendix A1 In Depth Analysis Of the Subjects’ Recordsmentioning

confidence: 99%

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Neamţu

Visa²,

Maniu

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

Cited by 1 publication

References 50 publications

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

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