Exploring message framing to engage parents in early screening for autism spectrum disorder

The 4.0 era education system requires the use of technology in pre-school and school activities. This study focuses on comparing the use of androin-based mobile applications for early detection of autism on user satisfaction (parents, health empowerment, teacher). This study was a quasi-experimental study with a post-test only control group design. A total of 30 parents, 30 health empowerment, and 30 educators in the city of Surakarta who were selected by purposive sampling were used as research samples. The samples were classified into two groups, namely by using an android-based mobile application for early detection of autism. User satisfaction data is collected through a user satisfaction questionnaire. Data were analyzed using univariate and bivariate with mann withney in the SPSS program. The results showed the influence of androin-based mobile applications early detection of autism on user satisfaction. The satisfaction of teaching staff users is higher than that of health empowerment and parents.

Section: Discussionmentioning

confidence: 99%

The Effectiveness of Android-Based Mobile Applications Authorized Early Detection on User Satisfaction (Parents, Health Empowerment, Teacher)

Prakosha

Supriyadi

Yunita

2021

IJMMU

“…The dataset contains 90 or 30.82% missing values in the age, ethnicity, and relation attributes. Age is one of the most significant information needed in ASD screening as it assists practitioners to determine the type of early treatment that the patients should be undergoing if they are diagnosed with ASD [ 13 , 41 , 42 ]. Thus, it is important to address the missing value.…”

Section: Methodsmentioning

confidence: 99%

Comparison of classification algorithms for predicting autistic spectrum disorder using WEKA modeler

Hassan

BMC Med Inform Decis Mak

2022

Background In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset. Result We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers’ based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset. Conclusion We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.

“…The dataset contains 90 or 30.82% missing values in the age, ethnicity, and relation attributes. Age is one of the most signi cant information needed in ASD screening as it assists practitioners to determine the type of early treatment that the patients should be undergoing if they are diagnosed with ASD (Delehanty et. al, 2020;Jones et.…”

Section: Datasetmentioning

confidence: 99%

Comparison of Classification Algorithms for Predicting Autistic Spectrum Disorder Using WEKA Modeler

Hassan

2021

Preprint

Background: In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.Result: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, and Deep Neural Network (DNN) algorithms to ASD screening dataset and compared the classifiers’ based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic (ROC), area under the curve (AUC), and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn will impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset. Conclusion: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.