Universal Journal of Public Health Vol. 10(2), pp. 226 - 240
DOI: 10.13189/ujph.2022.100209
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Compendium of Tunable Data-driven Approaches for Smoothing Parameter Learning for Kernel Density-based Normal Systolic Blood Pressure Detection

David Kwamena Mensah , Francis Eyiah-Bediako , Samuel Assabil ^*
Department of Statistics, University of Cape Coast, Ghana

ABSTRACT

The performance of a Kernel density-based approach for the detection of normal physiological vital signs can be affected drastically by the smoothing parameter that controls the underlying density. As a result, its application in practical (critical) areas such as public health, clinical trials and digital therapeutics (DTx), require some modifications for accurate and intelligent decisions. In this regard, this paper introduces a compendium of novel tunable data-driven smoothing parameter statistics together with tuning schemes for detecting normal systolic blood pressure (SBP) observations from continuously monitored SBP data. The tuning is designed based on both the entire data and observation-specific levels, using statistics that are by-products of the developed detector model, thereby allowing the smoothing parameters to adapt appropriately to the dynamics of the SBP data. A real systolic blood pressure data application illustrates the utility of the proposals, with significant improvement in overall performance over its well-known counterparts, the smoothed cross-validation bandwidth selector (HSCV), normal scale bandwidth selector (Hns), and Plug-in bandwidth selector (Hpi), implemented in standard statistical packages such as R. In particular, it turns out that observational-level tuning of smoothing parameters ensure better improvement in detection performance over the entire data tuning. Though performance assessment focused on accuracy, various problem-specific detection solutions can be designed for use in some application areas such as the pharmaceutical industries. For example, solution(s) based on sensitivity and specificity can be considered for early-stage clinical trials, for which several tuning proposals are evidently up to the task.

KEYWORDS
Smoothing Parameter, Data-based Tuning, Kernel Density Function, Moment, Systolic Blood Pressure

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] David Kwamena Mensah , Francis Eyiah-Bediako , Samuel Assabil , "Compendium of Tunable Data-driven Approaches for Smoothing Parameter Learning for Kernel Density-based Normal Systolic Blood Pressure Detection," Universal Journal of Public Health, Vol. 10, No. 2, pp. 226 - 240, 2022. DOI: 10.13189/ujph.2022.100209.

(b). APA Format:
David Kwamena Mensah , Francis Eyiah-Bediako , Samuel Assabil (2022). Compendium of Tunable Data-driven Approaches for Smoothing Parameter Learning for Kernel Density-based Normal Systolic Blood Pressure Detection. Universal Journal of Public Health, 10(2), 226 - 240. DOI: 10.13189/ujph.2022.100209.