Machine Learning Models for Early Prediction of Vascular Cognitive Impairment / 中国康复理论与实践

Objective:To explore the predictive performance of machine learning model based on vascular risk factors in early prediction of vascular cognitive impairment. Methods:From April to September, 2020, 70 subjects were enrolled and collected information of the demographics and vascular risk factors. They were assessed with Montreal Cognitive Assessment (MoCA), and then divided into normal group, vascular mild cognitive impairment (VaMCI) group and dementia group. The differences of vascular risk factors among the three groups were detected with one-way ANOVA, and the significant factors were selected to establish predictive models with support vector machine (SVM) and extreme learning machine (ELM). The predictive performance of two models was compared with Receiver Operating Characteristic Curve. Results:There were 32 cases in the normal group, 23 in VaMCI group and 15 in dementia group. Systolic blood pressure, fasting blood glucose, total cholesterol, low density lipoprotein and blood uric acid were significantly different among the three groups (F > 3.318, P < 0.05). The area under curve was the most (0.911) in SVM model predicting for VaMCI (P < 0.01), and the predictive efficacy was better for SVM model. Conclusion:SVM predictive model based on vascular risk factors may be more effective for predicting VaMCI.

Breast cancer diagnosis based on mammary thermography and extreme learning machines

Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.