Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study

Mac, Amanda; Tian, Xu; Wu, Joyce; Belousova, N.; Kitazawa, Haruna; Vozoris, Nicholas T.; Rozenberg, Dmitry; Ryan, Clodagh M.; Valaee, Shahrokh; Chow, Chung‐Wai

doi:10.1136/bmjresp-2022-001396

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…In this study, four types of classifiers were attempted and tenfold cross-validation was performed on all algorithms to prevent overfitting of the data 49 . An MLP, a type of ANN, is a network composed of multiple layers, including an input layer, multiple hidden layers, and an output layer 50 . The MLP yielded the best accuracy in this study.…”

Section: Discussionmentioning

confidence: 99%

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning

Lee,

Won,

Auh

et al. 2024

Sci Rep

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Xerostomia may be accompanied by changes in salivary flow rate and the incidence increases in elderly. We aimed to use machine learning algorithms, to identify significant predictors for the presence of xerostomia. This study is the first to predict xerostomia with salivary flow rate in elderly based on artificial intelligence. In a cross-sectional study, 829 patients with oral discomfort were enrolled, and six features (sex, age, unstimulated and stimulated salivary flow rates (UFR and SFR, respectively), number of systemic diseases, and medication usage) were used in four machine learning algorithms to predict the presence of xerostomia. The incidence of xerostomia increased with age. The SFR was significantly higher than the UFR, and the UFR and SFR were significantly correlated. The UFR, but not SFR, decreased with age significantly. In patients more than 60 years of age, the UFR had a significantly higher predictive accuracy for xerostomia than the SFR. Using machine learning algorithms with tenfold cross-validation, the prediction accuracy increased significantly. In particular, the prediction accuracy of the multilayer perceptron (MLP) algorithm that combined UFR and SFR data was significantly better than either UFR or SFR individually. Moreover, when sex, age, number of systemic diseases, and number of medications were added to the MLP model, the prediction accuracy increased from 56 to 68%.

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Section: Discussionmentioning

confidence: 99%

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning

Lee,

Won,

Auh

et al. 2024

Sci Rep

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“…We propose an nPCA approach to hold richer structural information of the raw data than PCA. The proposed approach is based on a multilayer perceptron (MLP), which is a type of artificial neural network (ANN) ( Mac et al, 2022 ). The nPCA we designed comprises an input layer, three hidden layers, and an output layer.…”

Section: Methodsmentioning

confidence: 99%

nPCA: a linear dimensionality reduction method using a multilayer perceptron

Li,

Wang

2024

Front. Genet.

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Background: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of data. Several dimension reduction methods have been developed, such as linear-based principal component analysis (PCA), nonlinear-based t-distributed stochastic neighbor embedding (t-SNE), and deep-learning-based autoencoder (AE). However, PCA only determines the projection direction with the highest variance, t-SNE is sometimes only suitable for visualization, and AE and nonlinear methods discard the linear projection.Results: To retain the linear projection of raw data and generate a better result of dimension reduction either for visualization or downstream analysis, we present neural principal component analysis (nPCA), an unsupervised deep learning approach capable of retaining richer information of raw data as a promising improvement to PCA. To evaluate the performance of the nPCA algorithm, we compare the performance of 10 public datasets and 6 single-cell RNA sequencing (scRNA-seq) datasets of the pancreas, benchmarking our method with other classic linear dimensionality reduction methods.Conclusion: We concluded that the nPCA method is a competitive alternative method for dimensionality reduction tasks.

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Extending the horizon in cardiovascular risk prediction: A synergy of conventional predictors and spirometry data

Nevzorova,

Shumatov,

Priseko

et al. 2024

Tihookeanskij medicinskij žurnal

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Aim. To assess a correlation between conventional cardiovascular risk factors and unprovoked pulmonary volumes and flows.Material and methods. The subjects (n = 153, median age 43 [32; 51] years) were divided into two groups depending on age. Questionnaires, anthropometry, biochemical blood analysis, spirometry without bronchodilation test, calculation of the lower limit of normal (LLN – low limit of normal) were carried out. Using the Mann – Whitney U-test, the two groups were compared quantitatively. When analyzing four-field conjugacy tables, a comparison was performed using the Pearson criterion χ2. The direction and closeness of the correlation were estimated using Spearman’s rank correlation coefficient. A predictive model was developed using the linear regression method. The differences were considered statistically significant at p < 0.05.Results. In group I (median age 29 [26; 33] years), reliable connections were established between the volume of forced expiratory volume in one second and high-sensitivity C-reactive protein (hsCRP); modified Tiffno index (m-TI) and total cholesterol, lowdensity lipoproteins, glucose. When analyzing the LLN data, reliable associations between m-IT and glucose (p = 0.004), hsCRP (p = 0.011) were obtained. In persons over 40 years of age with a smoking index of more than 10 packs/year, a relationship was found between a decrease in LLN m-TI and LLN of forced expiratory flow between 25 and 75% of forced vital capacity and an increase in creatinine (p < 0.001) and lipoprotein (a) (p = 0.03), respectively. Conclusion. There are differences in the relationship between the function of external respiration and laboratory indicators of cardiovascular risk in people before and after 40 years of age, which makes it possible to individualize the prediction of chronic non-communicable diseases.

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Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study

Cited by 3 publications

References 25 publications

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning

nPCA: a linear dimensionality reduction method using a multilayer perceptron

Extending the horizon in cardiovascular risk prediction: A synergy of conventional predictors and spirometry data

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