A novel method for prediction of skin disease through supervised classification techniques

Meena, K.; Veni, N. N. Krishna; Deepapriya, B. S.; Vardhini, P. A. Harsha; Kalyani, B. J. D.; Sharmila, L.

doi:10.1007/s00500-022-07435-8

Cited by 4 publications

(1 citation statement)

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“…The reason for this step is to obtain the optimal predictive performance of a model [ 47 ]. Accuracy, precision, recall, and f1-score were calculated to analyze the algorithms’ performance and select the best algorithm [ 48 ]. To identify whether our model is an underfit or overfit model, we created learning curves on accuracy for training and validation data using k-fold cross-validation to train and test data.…”

Section: Methodsmentioning

confidence: 99%

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

Javaid

Sarfraz

Aftab

et al. 2023

IJERPH

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The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

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Section: Methodsmentioning

confidence: 99%