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Rakesh Kumar Arora, Manoj Kumar Gupta



In this research, usage of big data analytics in medicine and public health will be examined with the aim of improving health outcomes and direction of population health plans. Our research is realized by applying diverse machine learning algorithms. They include Canine Forest and Support Vector Machine (Support Vector Machine) plus K-Means Clustering and LSTM Networks. We analyze the healthcare data in terms of Electronic Health Records, Genomic Sequencing, Medical Imaging, Wearable Device Data and Sociology and Demography. The experiments have good performance, where Random Forest reach an average accuracy of 85% in predictive tasks for diseases, Support Vector Machine achieved the accuracy rate of 82%, and K-Means Clustering which produced a silhouette score value of 0. 88 and 80% in health outcome analysis LSTM neural networks demonstrate the same prediction performances. Evidence from these research indicates that, data based analytics has the potential to build evidence based decision making, contribute to good patient care and address public health challenges. The findings show that the effectual resolution of the matter of precision medicine and precision public health depends on thorough interdisciplinary collaboration and the implementation of the most successful vision.


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Manoj Kumar Gupta, R. K. A. (2024). BIG DATA IN MEDICINE AND PUBLIC HEALTH . Obstetrics and Gynaecology Forum, 34(3s), 1330–1335. Retrieved from

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