Markers of Alzheimer’s disease in handwriting

The aim of the research. Alzheimer’s disease is the most common form of dementia. One of the potential tools for early detection of the onset of the disease is the handwriting analysis. It can be a warning signal for a serious medical investigation. The dynamics of handwriting changes are also a good indicator of the progression of the disease and the eff ectiveness of therapy. Methods. The authors have developed two corresponding tests. The fi rst (AD-HS) allows the assessment of handwriting markers of cognitive impairment and Alzheimer’s disease from an available handwriting sample. The second (ADHC) is designed to assess dynamics by comparing two handwritten documents written at diff erent times. Results. The pilot study includes 16 patients who were found to be at diff erent stages of the disease by medical examination. They all provided old handwriting samples dated 10–20 years ago and new handwriting samples specifi cally written as part of the experiment. Evaluation of 36 handwriting characteristics showed that both tests were eff ective in identifying Alzheimer’s disease and its stage. The correlation between the handwriting analysis and the medical test result was 0.62. Conclusion. Further refi nement of the proposed tests and expansion of the research base will enable handwriting exercises to be incorporated into supportive therapy to slow the progression of the disease.

Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools

Autism spectrum disorder (ASD) is a challenging developmental condition that involves restricted and/or repetitive behaviors and persistent challenges in social interaction and speech and nonverbal communication. There is not a standard medical test used to diagnose ASD; therefore, diagnosis is made by looking at the child's developmental history and behavior. In recent years, due to the increase in diagnosed cases of ASD, researchers proposed software-based tools to aid in and expedite the diagnosis. Considering the fact that most of these tools rely on the use of classifiers, in study, random forest, decision tree, k-nearest neighbors, and zero rule algorithms are used as classifiers, and their performances are compared using well-known performance metrics. As proven in the study, random forest algorithm can provide higher accuracy than the others in the classification of ASD and can be integrated into a computer- or humanoid-robot-based system for automated prescreening and diagnosis of ASD in preschool children groups.

Overview in the Use of Audio-Visual Learning Media in the Japanese Language Training Program at the Training and Skills Institute (LPK)

This research was motivated by the success of the Hoshi Hikari Skills and Training Institute in Padang City in managing the Japanese language training program. This can be seen from the number of participants who passed each training, namely 95% of the participants who attended the training passed and 5% of most participants did not pass the medical test. This achievement cannot be separated from the program manager who is very competent in the field of training management at LPK Hoshi Hikaro, Padang City. This study aims to see an overview of the use of audio-visual learning media in the Japanese Language Training program based on aspects of preparation, implementation and evaluation. This type of research is descriptive quantitative research. The population in this study were training participants at LPK Hoshi Hikari Padang City, totaling 24 people. The sampling technique is Cluster Random Sampling, the sample is taken as many as 20 people. Data collection techniques using a questionnaire. While the data collection tool is a questionnaire. The data analysis technique uses the percentage formula. The results showed that (1) the description of the use of audio-visual learning media in the Japanese language training program at the Hoshi Hikari Training and Skills Institute (LPK) of Padang City in the preparation aspect was good, (2) the description of the use of audio-visual learning media in the Japanese language training program. at the Hoshi Hikari Training and Skills Institute (LPK) Padang City in the implementation aspect it was good, and (3) the description of the use of audio visual learning media in the Japanese Language training program at the Hoshi Hikari Training and Skills Institute (LPK) Padang City in the evaluation aspect was good

An Approach to detect multiple diseases using machine learning algorithm

There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavour, but it can eventually assist practitioners in making timely decisions regarding patients’ health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India’s doctor-to-population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, and Heart diseases.

MMDD-Ensemble: A Multimodal Data–Driven Ensemble Approach for Parkinson's Disease Detection

Parkinson's disease (PD) is the second most common neurological disease having no specific medical test for its diagnosis. In this study, we consider PD detection based on multimodal voice data that was collected through two channels, i.e., Smart Phone (SP) and Acoustic Cardioid (AC). Four types of data modalities were collected through each channel, namely sustained phonation (P), speech (S), voiced (V), and unvoiced (U) modality. The contributions of this paper are twofold. First, it explores optimal data modality and features having better information about PD. Second, it proposes a MultiModal Data–Driven Ensemble (MMDD-Ensemble) approach for PD detection. The MMDD-Ensemble has two levels. At the first level, different base classifiers are developed that are driven by multimodal voice data. At the second level, the predictions of the base classifiers are fused using blending and voting methods. In order to validate the robustness of the propose method, six evaluation measures, namely accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC), are adopted. The proposed method outperformed the best results produced by optimal unimodal framework from both the key evaluation aspects, i.e., accuracy and AUC. Furthermore, the proposed method also outperformed other state-of-the-art ensemble models. Experimental results show that the proposed multimodal approach yields 96% accuracy, 100% sensitivity, 88.88% specificity, 0.914 of MCC, and 0.986 of AUC. These results are promising compared to the recently reported results for PD detection based on multimodal voice data.

Classification of Attention Deficit Hyperactivity Disorder using Variational Autoencoder

Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD.

Just a Flu? Self-perceived infection mediates the link between conspiracy beliefs and Covid-19 health beliefs and behaviors

The Covid-19 pandemic has inspired many conspiracy theories, which are associated with detrimental health beliefs and behaviors (e.g. reduced physical distancing; decreased vaccination intentions). We propose a previously unrecognized mediator of these relationships: A self-perceived likelihood to already have experienced a Covid-19 infection. Results from a large sample ( N = 9033) revealed that self-perceived infections mediated the link between conspiracy beliefs and health beliefs and behaviors. These findings emerged independently of institutional distrust, and actual infections as indicated by a positive medical test. These findings suggest that conspiracy beliefs shape people’s interpretation of the physical signals of their own body.

The Likelihood Difference Heuristic and Binary Test Selection Given Situation-Specific Utilities

Consider the task of selecting a medical test to determine whether a patient has a particular disease. Normatively, this requires taking into account (i) the prior probability of the disease, (ii) the likelihood---for each available test---of obtaining a positive result if the medical condition is present or absent, respectively, and (iii) the utilities for both correct and incorrect treatment decisions based upon each possible test result. But these quantities may not be precisely known. Are there strategies that could help identify the test with the highest utility given incomplete information? Here we consider the Likelihood Difference Heuristic (LDH), a simple heuristic that selects the test with the highest difference between the likelihood of obtaining a true positive and a false positive test result, ignoring all other information. We prove that the LDH is optimal when the probability of the disease equals the therapeutic threshold, the probability for which treating the patient and not treating the patient have the same expected utility. By contrast, prominent models of the value of information from the literature, such as information gain, probability gain, and Bayesian diagnosticity, are not optimal under these circumstances. Further results show how, depending on the relationship of the therapeutic threshold and prior probability of the disease, it is possible to determine which likelihoods are more important for assessing tests' expected utilities. Finally, to illustrate the potential relevance for real-life contexts, we show how the LDH might be applied to choosing tests for screening of latent tuberculosis infection.

The Evaluation of Medical Services presented to Students of physical education and Sport Sciences College/ university of Baghdad

The research aimed at identifying the medical services present to the students of physical education and sport sciences/university of Baghdad. The researchers hypothesized significant statistical differences in medical services evaluation. They used the descriptive method on (274) forth year College of physical education and sport sciences college students, university of Baghdad for the academic year 2021 – 2022. Only (150) students’ questionnaire was used in this researcher. The questionnaire included eight axis; each axe consisted of five questions (safety and first aids, medical examination and periodical comprehensive medical test, injury treatment, healthy environment, contagious diseases control, sports nutrition, personal health, psychological care). The research came up with many conclusions and recommendations.