Atexture Classification Using Random Forest And Decision Tree

Razooq, Mohammed M.

doi:10.21015/vtcs.v7i2.337

Cited by 2 publications

(1 citation statement)

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“…Machine Learning (ML) techniques have been widely used for pattern recognition in different fields of health, including, for instance, the classification of patients with different types of Dengue fever using Decision Tree (DT) (Farooqui et al, 2014); Association Rule Mining (ARM) to find patterns of symptoms in patients infected with Dengue Hemorrhagic Fever (DHF) and Typhoid Fever (TF) (Siswanto et al, 2016); and the classifications of patients infected with the Swine Flu disease based on clinical data integrated in a Medical Diagnosis Software (Raval et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Introduction to Electric Energy Systems

Overbye,

Anderson

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Arboviral diseases are common worldwide. Infection with arboviruses can lead to serious health problems, even death in severe cases. Such health problems can be prevented by the early and correct detection of these arboviruses, but this is challenging due to the overlap of their symptoms. In this work, we benchmark different Machine Learning (ML) models to classify two types of arboviruses. We propose two distinct binary models: (i) a model to classify if the patient has arbovirus or another disease; and (ii) a model to classify if the patient has Dengue or Chikungunya.We configure and evaluate several ML models using hyperparameter optimization and feature selection techniques. The Random Forest and XGboost tree-based models present the best results with over 80% recall in the Chikungunya and Inconclusive classes.

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Section: Introductionmentioning

confidence: 99%