Early Detection of Developmental Disorders Through Machine Learning Algorithm
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Machine learning algorithms have the ability to analyze huge amounts of data and discover patterns that may not be visible to humans. Machine learning offers new hope for faster, more accurate, and cheaper screening for early detection of developmental disorders. This research was conducted with the aim of developing an effective and efficient machine learning algorithm for analyzing child development data. Apart from that, it is also to identify the most relevant features and indicators for the detection of early developmental disorders. The method used by researchers in researching the Detection of Developmental Disorders through Machine Learning Algorithms is to use a quantitative method. The data obtained by researchers was obtained from the results of distributing questionnaires. The distribution of questionnaires carried out by researchers was carried out online using Google From software. The results of data acquisition will also be tested again using the SPSS application. From the research results, it can be seen that this research is expected to produce a model that is not only accurate, but can also be implemented in the wider health system to provide maximum benefits for society. And can improve children's health by enabling faster detection and intervention. Ultimately, this may improve long-term outcomes for children with developmental disorders. From this study, researchers can conclude that with advances in information technology, machine learning-based applications can be accessed via mobile devices and online platforms, allowing initial screening to be carried out easily by parents and educators, even before consulting a medical professional. In recent years, machine learning (ML) technology has shown that it has enormous potential for application in various fields, including health and medical care.
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