Multiple Disease Prediction Using Machine Learning

Bhavya Tyagi; Dhruv Varshney; Samridhi Yadav; Ms. Vansika Gupta

doi:10.36948/ijfmr.2024.v06i02.17657

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
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.17657
Short DOI	https://doi.org/gtrsv7

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5 Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

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

Multiple Disease Prediction Using Machine Learning

Share this