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All Journal BULLET : Jurnal Multidisiplin Ilmu
Nazmul Islam, Md
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Humanities Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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AI-Driven Patient Outcome Prediction: Balancing Innovation And Ethics In Healthcare Lama, Rasmila; Sen Oo , Sen; Tamang, Birbal; Ara, Jinnat; Nazmul Islam, Md
BULLET : Jurnal Multidisiplin Ilmu Vol. 4 No. 1 (2025): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

Predictive analytics in healthcare has gained significant attention due to its ability to enhance decision-making, reduce hospital readmission rates, and improve patient outcomes. Machine learning (ML) plays a pivotal role in developing predictive models that analyze vast amounts of patient data to forecast health outcomes. This paper explores the application of ML techniques in healthcare predictive analytics, discusses commonly used algorithms, evaluates their effectiveness, and highlights challenges and future research directions. The integration of machine learning (ML) in predictive analytics enables the processing and analysis of vast amounts of patient data to identify patterns and predict health outcomes. This paper explores the application of ML techniques in healthcare predictive analytics, discusses commonly used algorithms, evaluates their effectiveness, and highlights challenges and future research directions. We present a case study using supervised learning models to predict patient readmission rates and compare their accuracy based on real-world healthcare datasets. The findings indicate that ML-driven predictive analytics can significantly enhance healthcare efficiency, reduce costs, and improve patient care through early intervention and risk mitigation strategies.
Machine Learning Methodologies for Electric Vehicle Energy Management Strategies Khaledur Rahman, Md; Saiful Islam, Md; Jakaria Talukder, Md; Nazmul Islam, Md; Shameem Ahsan, Md
BULLET : Jurnal Multidisiplin Ilmu Vol. 4 No. 1 (2025): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

This research study explores the usage of machine learning techniques in the improvement of energy executives’ strategies for electric vehicles (EVs), with a particular accentuation on estimating EV-related variables and classifying price ranges. The study uses machine learning such as linear regression, random forest regression, decision tree, random forest classifier, and artificial neural network (ANN). The dataset involves fundamental electric vehicle (EV) attributes, including acceleration time, maximum speed, range, efficiency, and fast charging capacity. Information readiness includes the chores of handling missing values and changing category labels into a numerical column. The evaluation measures incorporate mean squared error, R-squared, and accuracy. The outcomes exhibit the efficacy of machine learning models in estimating EV-related variables and classifying price levels. The key discoveries highlight the unique performance of regression and classification models. This examination upgrades the cognizance of machine learning applications in EV energy the executives and gives important bits of knowledge to further develop determining accuracy and decision-making processes.
Co-Authors Ara, Jinnat Jakaria Talukder, Md Khaledur Rahman, Md Lama, Rasmila Saiful Islam, Md Sen Oo , Sen Shameem Ahsan, Md Tamang, Birbal