Fuad Ali Mohammed Al-Yarimi, Nabil Mohammed Ali Munassar and Fahd N. Al-Wesabi
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are…
Abstract
Purpose
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.
Design/methodology/approach
Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.
Findings
From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.
Originality/value
The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.
Details
Keywords
This paper aims to identify the factors influencing the adoption of financial technology (FinTech) services among Indian residents. Moreover, it compares the awareness levels…
Abstract
Purpose
This paper aims to identify the factors influencing the adoption of financial technology (FinTech) services among Indian residents. Moreover, it compares the awareness levels among both male and female users to offer a comprehensive insight into FinTech adoption.
Design/methodology/approach
The research comprises two cross-sectional surveys utilizing self-administered questionnaires: Study A involves 411 male participants and Study B involves 473 female users in FinTech adoption. This article used a “Statistical Package for Social Science (SPSS) followed by partial least squares-structural equation modeling (PLS-SEM)” for data analysis.
Findings
The exciting finding reveals that attitude and personal innovativeness have a significant impact, while technology anxiety shows a statistically insignificant impact on awareness in both studies. Surprisingly, the socio-demographic factor significantly impacts awareness (in Study A) and has an insignificant impact on awareness in Study B. Moreover, both studies reveal that awareness significantly impacts perceived usefulness and ease of use. Additionally, the outcomes confirm a positive relation between awareness, perceived usefulness, ease of use and FinTech adoption in both studies.
Practical implications
The present research will offer valuable insights to all FinTech service providers and stakeholders, aiding them in planning and designing relevant policies.
Originality/value
As far as the researchers are aware, this study stands as the initial survey into FinTech that specifically examines the impact of gender on technology adoption. The divergence in awareness and adoption rates between males and females and the authors’ insightful findings illuminate the context's uniqueness. Moreover, this article offers a robust model for using FinTech services from the perspective of a developing economy.