Dr. Nurliyana Binti Juhan
Dr. Nurliyana Binti Juhan
Pusat Persediaan Sains Dan Teknologi · Pusat Persediaan Sains Dan Teknologi, UMS
liyana87@ums.edu.my
Summary

My research expertise is anchored in the fields of Medical Statistics, Data Analysis, and Bayesian Modelling, where I have dedicated my efforts to advancing statistical methodologies in the context of medical research. With a keen focus on extracting meaningful insights from complex healthcare datasets, my work contributes to evidence-based decision-making and enhances the overall landscape of medical inquiry. Beyond the realm of Medical Statistics, my research portfolio extends into diverse areas, including Mathematics Education and Machine Learning. This multidisciplinary engagement underscores my commitment to exploring innovative intersections and applying statistical techniques across various domains, fostering a comprehensive and adaptable approach to research.

Dr. Nurliyana Binti Juhan holds DOKTOR FALSAFAH (MATEMATIK) from Universiti Teknologi Malaysia (Skudai) , among other qualifications, and has established themselves as a respected expert in their field.

Education
Doktor Falsafah (matematik)
Ijazah Sarjana Sains (statistik)
Ijazah Sarjana Muda Pendidikan Dengan Kepujian (pendidikan Dengan Sains)
Stats
Publications:
23
Projects:
11
Grants:
RM 938,462.99
Scopus Metrics
Scopus Author ID:
55913868100
H-Index:
3
Documents:
20
Citations:
34
Research Interests
BIOINFORMATICS - Epidemiological and Disease Modelling
EDUCATION - STEM Education
INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) - Predictive Analytics in Machine Learning
STATISTICAL STUDIES - Bayesian Statistics
Latest Grants
Edutech Stem Analytics: Empowering Stem With Iot, Drones & Computational Thinking In Sabah
Comparison Of Supervised Learning Algorithms In Identifying Influential Factors In Students’ Mathematics Performance.
Meningkatkan Enrolmen Pelajar Dalam Bidang Berkaitan Sains, Teknologi, Kejuruteraan Dan Matematik (stem) Melalui Pelaksanaan Program Promosi Dan Kesedaran Melibatkan Sekolah Awam Di Sabah Malaysia
Statistical Modelling Of Disease Prognosis Based On Complete Blood Count Data
Bayesian Approach To Support Triaging Of Renal Disease And Diabetes Patients Using Complete Blood Count Test Data
Latest Publications
Complete Blood Count-based Predictive Modelling For Diabetes Mellitus Among Malaysian Women: Implications For Public Health Screening
Bayesian Logistic Regression To Explore The Role Of Complete Blood Count In Kidney Disease Mortality (regresi Logistik Bayesian Untuk Meneroka Peranan Kiraan Darah Lengkap Dalam Kematian Penyakit Ginjal)
Application Of Exploratory And Confirmatory Factor Analysis To Model Loneliness Dimensions Among Pre-university Students
Investigating The Relationship Between Mental Health And Physical Activity: Insights From Phq-9 And Ipaq Data
Modelling Malaysian Mortality Improvement Using A Hybrid Logistic Spline
Administrative Positions
Pusat Persediaan Sains Dan Teknologi