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Medicolite-Machine Learning-Based Patient Care Model.

Rijwan Khan1, Akhilesh Kumar Srivastava2, Mahima Gupta1

  • 1Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, U.P. Affiliated to AKTU Lucknow, U.P., India.

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Machine learning enhances healthcare through the "Medicolite" app, offering diet advice, remote appointments, and medication delivery. A CNN classifier proved faster than SVM for processing sensitive patient data securely.

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Area of Science:

  • Computer Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • The integration of machine learning (ML) into healthcare systems offers transformative potential for patient care and operational efficiency.
  • Current healthcare systems often face challenges with data management, accessibility, and timely medical consultations.
  • The development of smart healthcare solutions is crucial for proactive health management and personalized treatment.

Purpose of the Study:

  • To explore the impact of machine learning on healthcare delivery.
  • To introduce "Medicolite," a novel healthcare application designed to address common health issues and improve accessibility to medical services.
  • To evaluate the performance of a Convolutional Neural Network (CNN)-based classifier against a Support Vector Machine (SVM) classifier for healthcare data processing.

Main Methods:

  • Development of the "Medicolite" application with modules for diet management, online appointments, and medication services.
  • Implementation of machine learning algorithms for intelligent data processing and response generation.
  • Comparative analysis of a CNN-based classifier and an SVM-based classifier using training and testing sessions.

Main Results:

  • The "Medicolite" application facilitates remote healthcare access, including consultations and medication.
  • Machine learning enables intelligent data analysis and personalized health responses, reducing manual intervention.
  • The CNN-based classifier demonstrated superior speed, completing training and testing in 30 seconds compared to SVM's 58 seconds.

Conclusions:

  • Machine learning significantly enhances healthcare by enabling smart decision-making and efficient data handling.
  • "Medicolite" represents a practical application of these technologies, improving patient convenience and access to care.
  • CNN-based classifiers offer a faster and more efficient alternative to SVM for time-sensitive healthcare applications.