NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization

Nesterov, A. Yu.; Ibragimov, B. Sh.; Umerenkov, Dmitry; Shelmanov, Artem; Rybalchenko, V.M.; Kokh, Vladimir

doi:10.1007/978-3-031-09342-5_8

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The final work compared is the one developed by Nesterov et al [35]. The results obtained in this work seem better than those obtained in this study, as the dataset used for that work varies between 5 to 10 times the size of the dataset used here.…”

Section: Resultsmentioning

confidence: 61%

“…In our work, a 99.19% is obtained with a first hidden layer of 50 neurons (less than a 1%) and a second hidden layer of 50 neurons too (less than a 2%). So, in comparison, the network used by Nesterov et al [35] has 100 times more neurons than ours to classify 4 times the illnesses used in this work.…”

Section: Resultsmentioning

confidence: 77%

“…However, the work presented by Nesterov et al [35] uses a first hidden layer of more than 6000 neurons and a second layer of 3000 neurons. In our work, a 99.19% is obtained with a first hidden layer of 50 neurons (less than a 1%) and a second hidden layer of 50 neurons too (less than a 2%).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Disease screening using Artificial Intelligence

Fuster-Palà,

Luna-Perejón,

Domínguez-Morales

2024

Preprint

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This work presents a disease detection classifier based on symptoms encoded by their severity. This model is presented as part of the solution to the saturation of the healthcare system, aiding in the initial screening stage. An open-source dataset was used, which underwent pre-processing and served as the data source to train and test various machine learning models, including SVM, Random Forests, KNN, and ANNs. A 3-phase optimization process is developed to obtain the best classifier: first, the dataset is pre-processed; secondly, a grid search is performed with several hypermarameters variations to each classifier; and, finally, the best models obtained are subjected to an additional filtering processes. The best-results model, selected based on the performance and the execution time, is a KNN with 2 neighbors, which achieves an accuracy and F1 score of over 98\%. These results demonstrate the effectiveness and improvement of the evaluated models compared to previous studies, particularly in terms of accuracy. Although the ANN model has a longer execution time compared to KNN, it is retained in the work due to its potential to handle more complex datasets in a real clinical context.

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Section: Resultsmentioning

confidence: 61%

Section: Resultsmentioning

confidence: 77%