International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.

Multiple Disease Prediction Using Machine Learning

Author(s) Bhavya Tyagi, Dhruv Varshney, Samridhi Yadav, Ms. Vansika Gupta
Country India
Abstract In various regions worldwide, the incidence of life-threatening diseases such as brain tumors, cataracts, pneumonia, and malaria remain alarmingly high, posing significant challenges to public health systems. Multiple factors contribute to the onset of these conditions, including genetic predispositions, environmental exposures, and lifestyle choices. This research endeavors to develop and evaluate a data-driven predictive model for early detection of brain tumors, cataracts, pneumonia, and malaria utilizing convolutional neural network (CNN) algorithms trained on medical imaging data. The proposed method integrates diverse patient parameters, including demographic information, medical history, and imaging features, to forecast the risk of developing these diseases. CNN architecture is chosen as the preferred model due to its ability to effectively analyze complex image data. Ethical considerations and privacy concerns regarding the handling of sensitive medical information are thoroughly examined, emphasizing the importance of responsible model development. Furthermore, the interpretability of CNN models is addressed to facilitate understanding among healthcare professionals and patients. The developed predictive system demonstrates promising accuracy and reliability, with CNN achieving notable performance metrics across all disease categories. A web-based platform is implemented to facilitate easy input and disease prediction based on medical images. The dataset utilized in this study is sourced from reputable medical institutions and research organizations, ensuring data quality and integrity. The findings of this research contribute valuable insights into the application of CNN-based predictive models in healthcare, offering a pathway for integrating such systems into clinical practice for early disease diagnosis and intervention
Keywords Brain tumor, Cataract, Pneumonia, Malaria, Medical imaging, Convolutional neural network (CNN), Predictive modeling, Early detection, Healthcare, Ethical considerations, Privacy concerns, Interpretability, Data-driven approach, Machine learning, Disease prediction, Web-based platform.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-21
Cite This Multiple Disease Prediction Using Machine Learning - Bhavya Tyagi, Dhruv Varshney, Samridhi Yadav, Ms. Vansika Gupta - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.17657
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