Machine learning (ML) techniques have gained prominence in recent years for their potential to improve healthcare outcomes, including the prediction and prevention of stroke. The primary objective of this study is to develop and validate a robust ML model for the prediction and early detection of stroke in the brain. This study aimed to address some of the limitations of previous studies by utilizing a representative dataset and applying proper feature engineering techniques. The results showed that the Artificial Neural Network (ANN) model with synthetic minority oversampling technique (SMOTE) (ratio =0.3) and hyperparameter turning performs the best in terms of average precision, which is crucial for making accurate predictions in clinical settings. By adjusting the threshold for classification, the model can be tailored to achieve the desired balance between precision and recall, depending on the specific requirements of a clinical workflow.

Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. With no known cures, there is a pressing need to find behavioral tasks and biomarkers that can accurately assess and/or predict disease progression in asymptotic patients, as treatment is likely to be most effective at an early stage of AD. On the other hand, artificial intelligence systems are powerful and critical tools to support early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation in healthcare. In this study, standard neuropsychological tests and a simple 5.5-minute cognitive task were administered to patients with mild AD or mild cognitive impairment (MCI) (AD group, n=28) and cognitively normal older adults (Control group, n=50). Patients with mild AD or MCI were collapsed together as the AD group. Four different machine learning algorithms were applied to classify patients from healthy controls using the data collected from neuropsychological tests, or the cognitive task, or both. The results of the study revealed that machine learning technique has the potential to assist AD diagnosis using the neuropsychological data, and when combining the neuropsychological and cognitive data, the classification accuracy can be further improved.

The majority of stroke survivors suffer from residual functional deficits in the Upper Extremity (UE) that limits the patients' ability to incorporate the hemiparetic UE into daily function such as reaching and grasping objects. Correctly assessing the degree to which individuals with stroke use their UE would be important for rehabilitation. However, previous work shows that many stroke patients, when observed in the laboratory, can appear to use the paretic arm with adequate ability, yet do not use the arm at home with the expected regularity. The main purposes of this study are to use various machine learning techniques to examine associations between laboratory-based measures of limb functioning and actual home use. The UE kinematics during reaching and grasping tasks were measured in the laboratory, while the use of the paretic arm in the home environment was assessed with portable accelerometry and the Motor Activity Log (MAL). The study identified a subset of the biomechanical features that predicted well the paretic arm use at home. Arm use was also well predicted with clinical evaluations of impairment using Fugl-Meyer and Action Research Arm Test (ARAT) scores. However, combining the clinical and a subset of biomechanical features yielded the best model to predict UE use in individuals with stroke.

Upper Extremity (UE) rehabilitation is often needed post-stroke. The main goal of UE treatment in stroke survivors is to increase the use of the affected UE in the home and community. However, the effectiveness of UE treatments are difficult to quantify because no objective evaluation of UE use exists. In practice, a clinician rates the patient's ability to perform specific motor tasks associated with functional use in a clinic or the patient self-reports the amount or quality of arm movement for a standard set of activities. Both methods do not objectively measure the performance of the affected UE in the home or community environment, and there is growing evidence that motor performance in the laboratory is a poor proxy for the actual amount of UE use. Using a single wrist-worn sensor (i.e., accelerometry data) and machine learning, we have reported that it is possible to separate UE functional use from nonfunctional movement after stroke. Specifically, we reported that we correctly classified sensor data with an average of 94.80% in controls and 88.38% in stroke subjects in intra-subject test trials, and 91.53% for controls and 70.18% in stroke subjects in inter-subject test trials. In this paper, we employed feature selection techniques and explored different machine learning methods to improve the classification accuracy. Our enhanced methods are robust and reliable, and work in both intra-subject and inter-subject training and testing. Our result showed better accuracy in stroke patients than previously reported with the same dataset. The enhanced models reached an average of 96% accuracy in control subjects and 94% in stroke subjects for intra-subject trials, and an average of 90% accuracy in control subjects and 83% in stroke subjects for the inter-subject trials. The proposed methods provide an inexpensive and feasible way to quantify the UE functional use in home and community. This information can provide guidance for clinical practice in the rehabilitative care of adults recovering from stroke.