Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico

Abstract

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due to the limited space in the emergency department, it is important to identify when thoracic pain is of cardiac origin, since being a symptom of CVD (Cardiovascular Disease), the attention to the patient must be immediate to prevent irreversible injuries or even death. Artificial intelligence contributes to the early detection of pathologies, such as chest pain. In this study, the machine learning techniques were used, performing an analysis of 27 variables provided by a database with information from 258 geriatric patients with 60 years old average age from Medical Norte Hospital in Tijuana, Baja California, Mexico. The objective of this analysis is to determine which variables are correlated with thoracic pain of cardiac origin and use the results as secondary parameters to evaluate the thoracic pain in the emergency rooms, and determine if its origin comes from a CVD or not. For this, two machine learning techniques were used: Tree classification and cross-validation. As a result, the Logistic Regression model, using the characteristics proposed as second factors to consider as variables, obtained an average accuracy (μ) of 96.4% with a standard deviation (σ) of 2.4924, while for F1 a mean (μ) of 91.2% and a standard deviation (σ) of 6.5640. This analysis suggests that among the main factors related to cardiac thoracic pain are: Dyslipidemia, diabetes, chronic kidney failure, hypertension, smoking habits, and troponin levels at the time of admission, which is when the pain occurs. Considering dyslipidemia and diabetes as the main variables due to similar results with machine learning techniques and statistical methods, where 61.95% of the patients who suffer an Acute Myocardial Infarction (AMI) have diabetes, and the 71.73% have dyslipidemia.

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

“…For more accurate results, it was suggested to compare patients’ responses with responses collected by healthy individuals [ 26 ]. In a case series study, Rojas-Mendizabal et al performed an analysis of 27 variables, which included demographic and clinical parameters, in order to evaluate the origin of thoracic pain and determine a possible correlation between these parameters and the presence of cardiac pain, managing to obtain a mean accuracy of 96% [ 29 ]. Rogachov et al acquired resting-state functional MRI and quantified frequency-specific regional low-frequency oscillations (LFOs) in patients with chronic pain and ankylosing spondylitis and, using an ML approach, found that higher frequencies can be used to make generalizable inferences about patients’ average pain ratings (trait-like pain) but not current (i.e., state-like) pain levels [ 30 ].…”

“…Decision trees (DT) [14,22,29,34] Gradually reject classes assigned into multistage decision systems to accept a final class. In pain medicine, decision trees algorithms such as classification and regression trees have been used…”

“…k-means clustering [14] Divides a number of data points into a number of clusters based on the nearest mean k-nearest neighbors (kNN) [10,22,24,27,29,32] Assigns data patterns to a class on the basis of the distance to the training patterns of a certain class Multilayer perceptron (MLP) [9,22] Trains on a set of input data patterns to predict/classify the output class Random forest (RF) [14,15,28,29,31,32] Builds and merges multiple decision trees to provide a more accurate prediction Regression: kernel ridge regression (KRR), [30]; elastic net (EN) [23,28]; generalized linear mixed-models (GLMMs) based on repeated data points, Lasso [15,24]; least square (LS) [28]; linear regression (LiR) [27,33]; logistic regression (LoR) [10,15,29,31,32]; ridge regression (RR) [28] Predicts the probability of agreement using continuous data points Support vector machine (SVM) [9, 12, 15, 21, 22, 25-27, 29, 32, 34] Creates a hyperplane to separate two classes. The hyperplane is found by optimizing a cost function Multi-subject dictionary learning (MSDL) [16] It is a feature learning method where a training example is represented as a linear combination of basic functions, and is assumed to be a sparse matrix medical hypotheses, etc.…”

“…Table 2 presents the characteristics of the studies, including the ML method that was applied and the respective results. ML techniques for classifying the intensity of pain were found to be effective in patients with low back pain (LBP) [9,12,13,16], osteoarthritis [21], ankylosing spondylitis [30], spinal cord injury [26], thoracic pain [29], sickle cell disease (SCD) [32], evoked heat pain [25,27], and other types of pain [15]. In their case-controlled study, Abdollahi et al classified pain based on quantitative kinematic data.…”

“…A subclass of NN is the convolutional neural network (CNN) Decision trees (DT) [ 14 , 22 , 29 , 34 ] Gradually reject classes assigned into multistage decision systems to accept a final class. In pain medicine, decision trees algorithms such as classification and regression trees have been used k-means clustering [ 14 ] Divides a number of data points into a number of clusters based on the nearest mean k-nearest neighbors (kNN) [ 10 , 22 , 24 , 27 , 29 , 32 ] Assigns data patterns to a class on the basis of the distance to the training patterns of a certain class Multilayer perceptron (MLP) [ 9 , 22 ] Trains on a set of input data patterns to predict/classify the output class Random forest (RF) [ 14 , 15 , 28 , 29 , 31 , 32 ] Builds and merges multiple decision trees to provide a more accurate prediction Regression: kernel ridge regression (KRR), [ 30 ]; elastic net (EN) [ 23 , 28 ]; generalized linear mixed-models (GLMMs) based on repeated data points, Lasso [ 15 , 24 ]; least square (LS) [ 28 ]; linear regression (LiR) [ 27 , 33 ]; logistic regression (LoR) [ 10 , 15 , 29 , 31 , 32 ]; ridge regression (RR) [ 28 ] Predicts the probability of agreement using continuous data points Support vector machine (SVM) [ 9 , ...…”