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Zakia Sultana Munmun on Muck Rack

Zakia Sultana Munmun

As seen in: Scientific Research Publishing (SCIRP)
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Articles

Machine Learning-Based Classification of Coronary Heart Disease A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models

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By Zakia Sultana Munmun, Salma Akter, Chowdhury Raihan Parvez
| Scientific Research Publishing (SCIRP)
Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models () 1. Introduction Coronary heart disease (CHD) remains the leading cause of death globally. It develops when fatty deposits, known as atheroma, accumulate in coronary arteries, impeding blood flow and potentially leading to severe complications such as heart attacks.
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Artificial Intelligence and Big Data for Personalized Preventive Healthcare Predicting Health Risks and Enhancing Patient Adherence

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By Zakia Sultana Munmun, Ripa Akter, Bilkish Nurani, Foysal Kabir
| Scientific Research Publishing (SCIRP)
1. Introduction Personalized preventive healthcare represents a paradigm shift in the delivery of healthcare services, aiming to provide tailored interventions based on an individual’s unique health profile and risks. The integration of Artificial Intelligence (AI) and Big Data analytics has revolutionized healthcare by enabling more accurate predictions of disease risks and optimizing personalized treatment plans.
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Predictive Healthcare An IoT-Based ANFIS Framework for Diabetes Diagnosis

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By Jennet Atayeva, Omar Faruq, Zakia Sultana Munmun, Sohel Mollick
| Scientific Research Publishing (SCIRP)
1. Introduction The advent of the Internet of Things (IoT) has transformed numerous industries, particularly healthcare, by enabling seamless data collection and real-time monitoring of patients. IoT systems connect medical devices and sensors, providing healthcare professionals with critical, real-time information about patients’ health status. This innovation has the potential to significantly improve the efficiency of diagnosis, treatment, and patient care [1].
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