Human Disease Prediction Using Machine Learning

Artificial intelligence (AI) is gradually transforming the landscape of medical practice. Advances in digitized data gathering, machine learning, and computational resources have made it easier to integrate AI into fields traditionally dominated by humans. This paper presents an overview of recent a...

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Bibliographic Details
Published in2024 International Conference on Emerging Smart Computing and Informatics (ESCI) pp. 1 - 6
Main Authors Mane, Vijay, Jadhav, Mohinee, Kadam, Ishita
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.03.2024
Subjects
Biological system modeling
CNN
Libraries
Machine learning
Machine learning algorithms
Numpy
Prediction algorithms
Random Forest
Scipy
Sociology
Software algorithms
XGBoost
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DOI10.1109/ESCI59607.2024.10497303

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Summary:Artificial intelligence (AI) is gradually transforming the landscape of medical practice. Advances in digitized data gathering, machine learning, and computational resources have made it easier to integrate AI into fields traditionally dominated by humans. This paper presents an overview of recent advancements in AI technologies, their utilization in biomedical contexts, and examines the economic and social ramifications of AI in healthcare. Python, employed in our work, boasts an expansive and all-encompassing assortment of freely accessible packages that span a diverse range of subjects. Scientific Python libraries like NumPy, SciPy, and pandas offer proficient implementations for numerical operations and tasks commonly encountered in science and engineering. These libraries offer a robust foundation for the development of more sophisticated scientific software, alleviating the need to concern oneself with low-level algorithms. Also, we have used CNN to classify various diseases and also some Machine Learning (ML) algorithms such as Random Forest and XGBoost classifier.
DOI:10.1109/ESCI59607.2024.10497303