Musculoskeletal and neurological disorders are common devastating companions of ageing, leading to a reduction in quality of life and increased mortality. Gait analysis is a popular method for diagnosing these disorders. However, manually analysing the motion data is a labour-intensive task, and the quality of the results depends on the experience of the doctors. In this paper, we propose an automatic framework for classifying musculoskeletal and neurological disorders among older people based on 3D motion data. We also propose two new features to capture the relationship between joints across frames, known as 3D Relative Joint Displacement (3DRJDP) and 6D Symmetric Relative Joint Displacement (6DSymRJDP), such that relative movement between joints can be analyzed. To optimize the classification performance, we adapt feature selection methods to choose an optimal feature set from the raw feature input. Experimental results show that we achieve a classification accuracy of 84.29% using the proposed relative joint features, outperforming existing features that focus on the movement of individual joints. Considering the limited open motion database for gait analysis focusing on such disorders, we construct a comprehensive, openly accessible 3D full-body motion database from 45 subjects.

The population of older people in the world has grown rapidly in recent years. To alleviate the increasing burden on health systems, automated health monitoring of older people can be very economical for requesting urgent medical support when a harmful accident has been detected. One of the accidents that happens frequently to older people in a household environment is a fall, which can cause serious injuries if not handled immediately. In this paper, we propose a motion classification approach to fall detection, by integrating the techniques of motion capture and machine learning. The motion of a person is recorded with a set of inertial sensors, which provides a comprehensive and structural description of body movements, while being robust to variations in the working environment. We build a database comprising motions of both falls and normal activities. We experiment with several combinations of joint selection, feature extraction, and classification algorithms, showing that accurate fall detection can be achieved by our motion classification approach.

The early detection of dementia, a condition affecting both individuals and society, is essential for its effective management. However, reliance on advanced laboratory tests and specialized expertise limits accessibility, hindering timely diagnosis. To address this challenge, this study proposes a novel approach in which readily available biochemical and physiological features from electronic health records are employed to develop a machine learning-based binary classification model, improving accessibility and early detection. A dataset of 14,763 records from Phachanukroh Hospital, Chiang Rai, Thailand, was used for model construction. The use of a hybrid data enrichment framework involving feature augmentation and data balancing was proposed in order to increase the dimensionality of the data. Medical domain knowledge was used to generate inter-relation-based features (IRFs), which improve data diversity and promote explainability by making the features more informative. For data balancing, the K-Means Synthetic Minority Oversampling Technique (K-Means SMOTE) was applied to generate synthetic samples in under-represented regions of the feature space, addressing class imbalance. Extra Trees (ET) was used for model construction due to its noise resilience and ability to manage multicollinearity. The performance of the proposed method was compared with that of Support Vector Machine, K-Nearest Neighbors, Artificial Neural Networks, Random Forest, and Gradient Boosting. The results reveal that the ET model significantly outperformed other models on the combined dataset with four IRFs and K-Means SMOTE across key metrics, including accuracy (96.47%), precision (94.79%), recall (97.86%), F1 score (96.30%), and area under the receiver operating characteristic curve (99.51%).

Cardiovascular diseases are chronic diseases that cause serious morbidity and mortality worldwide. Unfortunately, the patients with type 2 diabetes mellitus and hypertension have a high risk of having a cardiovascular complication. For these reasons, patients with type 2 diabetes mellitus and hypertension should be aware of cardiovascular complication along their healthcare journey. To prevent cardiovascular complication from diabetes and hypertension, accurate risk prediction is required for a long term self-management process. Consequently, this paper proposes a fuzzy logic based method for predicting cardiovascular risk particularly for a patient with type 2 diabetes mellitus and hypertension. This paper also proposes a set of factors based on the patient’s lifestyle as the key factors besides clinical factors because of their implicit impact on the quality of life of the patient. The proposed model thus employs 15 predictors for both clinical and lifestyle risk factors. Additionally, the proposed model is constructed based on the scientific data and implicit knowledge of the experts. The experiment with 121 patients shows that the proposed prediction model provides 96.69% accuracy compared to those decided by the experts.

This paper review an important of the student loan consideration criteria by using factor analysis method. The processes include five steps determining factors that influence the consideration; Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlet's test of sphericity, Principal components analysis (PCA), rotation, evaluation, and interpretation. The dataset is a historical data of student loan candidates collected between 2014 and 2015. There are 1,146 samples with seven criteria. Thus, the factor analysis can reduce the dimensions of variance in the sample by identifying the significant factors for student loan consideration. As the result, there are only three factors includes variable of family's income, amount of money the student got from other scholarship and education level of the sibling. After that, the clustering technique is used to group the data reduced to be only three factors. The result shows that, the cluster is not pure accepted or rejected students in the cluster, which is a same as using all seven factors. Therefore, there are an inappropriate of some factors in traditional student loan fund consideration.