Logit model in prospective coronary heart disease (CHD) risk factors prediction in Saudi population

Using a machine learning-based risk prediction model to analyze the coronary artery calcification score and predict coronary heart disease and risk assessment

OBJECTIVES

To calculate the coronary artery calcification score (CACS) obtained from coronary artery computed tomography angiography (CCTA) examination and combine it with the influencing factors of coronary artery calcification (CAC), which is then analyzed by machine learning (ML) to predict the probability of coronary heart disease(CHD).

METHODS

All patients who were admitted to the Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University from January 2019 to March 2022, suspected of CHD, and underwent CCTA inspection were retrospectively selected. The degree of CAC was quantified based on the Agatston score. To compare the correlation between the CACS and clinical-related factors, we collected 31 variables, including hypertension, diabetes, smoking, hyperlipidemia, among others. ML models containing the random forest (RF), radial basis function neural network (RBFNN),support vector machine (SVM),K-Nearest Neighbor algorithm (KNN) and kernel ridge regression (KRR) were used to assess the risk of CHD based on CACS and clinical-related factors.

RESULTS

Among the five ML models, RF achieves the best performance about accuracy (ACC) (78.96%), sensitivity (SN) (93.86%), specificity(Spe) (51.13%), and Matthew's correlation coefficient (MCC) (0.5192).It also has the best area under the receiver operator characteristic curve (ROC) (0.8375), which is far superior to the other four ML models.

CONCLUSION

Computer ML model analysis confirmed the importance of CACS in predicting the occurrence of CHD, especially the outstanding RF model, making it another advancement of the ML model in the field of medical analysis.

Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation

BACKGROUND

To construct a neural network model (ATBP) for predicting susceptibility to Post-inflammatory hyperpigmentation (PIH), which is a rapid, objective, and reliable decision-support method before physical and chemical interventions in dermatology clinics for pigment disorders.

MATERIAL AND METHODS

A dataset was established based on the VISIA Skin Analysis System detection results of 1953 patients with pigment disorders including 93,477 labeled data under 8 indicators. A novel Post-inflammatory hyperpigmentation susceptibility prediction model incorporating Multi-head self-attention mechanism and Back-propagation neural network is proposed to capture the patterns of skin detection data to predict PIH susceptibility.

RESULTS

The results of comparison experiments indicate that Attentive BP (Back Propagation Neural Network) has a significant superiority in prediction accuracy (0.8604) compared with other machine learning models. The ablation experiments prove that the Multi-head self-attention mechanism substantially improves the accuracy and the stability of prediction. The results of the 10-fold cross-validation experiment prove that ATBP is robust and avoids turbulence in predicting.

CONCLUSION

Leveraging Multi-head self-attention mechanism and the architecture advantage of BPNN, the proposed model ATBP obtains the robust and efficient prediction performance in predicting PIH susceptibility via processing large-scale and hi-dimension data, i.e., considering comprehensive skin conditions of individual patient. It can be proved from the experimental results that the proposed model is reliable for decision-support work of PIH susceptibility.