METHOD FOR DIAGNOSING MOSQUITO-BORNE DISEASES

Abstract

ABSTRACT A method (100) for diagnosing mosquito-borne diseases. Further, the method comprising implementing machine learning and deep learning models to analyze patient data. Further, the method (100) comprising the steps of utilizing symptom data and medical history to train said models. Further, the method (100) comprising the steps of achieving improved accuracy in diagnosing diseases such as Dengue, Chikungunya, and Malaria. Further, the method (100) comprising the steps of providing a cost-effective alternative to traditional diagnostic methods, thereby enhancing early detection and intervention capabilities in healthcare settings.

Specification

Description:
FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of public health informatics and, in particular, relates to a method for detecting mosquito-borne diseases using machine learning and deep learning technologies, aimed at improving diagnostic accuracy and efficiency while providing a cost-effective alternative to traditional diagnostic methods in resource-constrained settings.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Mosquito-borne diseases, such as Dengue, Chikungunya, and Mal , Claims:1. A method (100) for diagnosing mosquito-borne diseases, the method comprising the steps of:
implementing machine learning and deep learning models to analyze patient data;
utilizing symptom data and medical history to train said models;
achieving improved accuracy in diagnosing diseases such as Dengue, Chikungunya, and Malaria; and
providing a cost-effective alternative to traditional diagnostic methods, thereby enhancing early detection and intervention capabilities in healthcare settings.
2. The method (100) as claimed in claim 1, wherein accuracy of the developed machine learning models for Dengue and Chikungunya detection is enhanced through the use of a voting classifier that combines the predictions of multiple algorithms, resulting in an overall accuracy of 65%, while the deep learning model for Malaria achieves a training accuracy of 96.28% through the use of a Convolutional Neural Network (CNN) based on the VGG19 architecture.

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