Comparative Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection

Singh, Kushal; Thakur, Khushal; Kapoor, Divneet Singh; Sharma, Anshul; Bajpai, Sakshi; Sirawag, Neeraj; Mehta, Riya; Chaudhary, Chitransh; Singh, Utkarsh

doi:10.1007/978-981-19-9225-4_6

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM models excel in modeling nonlinear class boundaries by transforming input vectors into expansive feature spaces, especially adept for intricate data like text or images. Yet, with increasing dataset sizes, they become computationally intensive and less interpretable, particularly regarding hyperplane selection and parameter tuning 185,220 . Such “black box” traits can impede their broader acceptance in clinical domains.…”

Section: Machine Learning (Ml) Methods In Clinical Databasesmentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

View full text Add to dashboard Cite

BackgroundIncreasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data‐rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.MethodsWe conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.ResultsOur study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision‐making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.ConclusionAdvanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

show abstract

Section: Machine Learning (Ml) Methods In Clinical Databasesmentioning

confidence: 99%