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Big Data and Machine Learning in Healthcare: A Business Intelligence Approach for Cost Optimization and Service Improvement

Mohammad Tonmoy Jubaear Mehedy
Department of Information Technology, Washington University of Science and Technology (wust Eisenhower Ave, Alexandria VA 22314, USA
Muhammad Saqib Jalil
Management and Information Technology, St. Francis College, Brooklyn, New York, USA
Maham Saeed
Master of science in management Healthcare, St. Francis College, Brooklyn, New York, USA
Abdullah al mamun
Department of Business Analytics, St. Francis College, Brooklyn, New York, USA
Esrat Zahan Snigdha
Master’s of Business Administration, Health Care Management, Washington University of Science and Technology (wust), Eisenhower Ave, Alexandria VA 22314, USA
MD Nadil khan
Department of Information Technology, Washington University of Science and Technology (wust Eisenhower Ave, Alexandria VA 22314, USA.
Nahid Khan
East West University, Dhaka, Bangladesh
MD Mohaiminul Hasan
Department master’s in project information technology, St Francis College, Brooklyn, New York, USA
DOI Crossref DOI 10.37547/tajmspr/Volume07Issue03-14
tajmspr 2025
VOL. 7 / NO. 03 MARCH
FULL TEXT PP. 115-135
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VOLUME 7
ISSUE 03
YEAR 2025
PAGES 115-135
RESEARCH OVERVIEW

Abstract

Healthcare business intelligence advances through the combination of Big Data and Machine Learning (ML) technology which improves both cost reduction and service quality. Healthcare organizations employ predictive analysis together with AI-driven choices and real-time processing to minimize costs as global healthcare fees continue increasing while improving patient care efficiency. This paper investigates the transformation of resource distribution and predictive equipment maintenance and individual medical approaches through Big Data and ML models along with supervised learning and deep learning and anomaly detection algorithms. The research follows a quantitative approach to study both actual case examples and statistical models which predict hospital admissions while optimizing resource management to lower operational flaws. AI predictive analytics produces a 30% deduction in healthcare bills supported by studies with results showing also a 25% increase in medical service delivery efficiency. Real-time data integration allows medical professionals to detect diseases earlier and develop precise treatment plans for each patient which increases patient satisfaction rates. The study adds to existing AI-driven healthcare business intelligence research by delivering practical guidelines which healthcare administrators and policymakers and technology leaders can use. The paper requirement of data governance frameworks together with ethical AI implementation methods and scalable decision systems based on ML is necessary for achieving the complete benefits of Big Data in healthcare.

RESEARCH TOPICS

Keywords

Big Data Machine Learning Healthcare Business Intelligence Cost Optimization

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