Penerapan Teknik Stacking untuk Optimasi Deteksi Dini Anak Autis berbasis Support Vector Machine - Yulianti, Yulianti; Desyani, Teti; Mulyati, Sri (Jurnal Teknologi Sistem Informasi dan Aplikasi, 2024)
Autism is not difficult to detect, but it takes a lot of learning and training for doctors to detect it. Currently ASD is detected by understanding the behavior and intellectual activity of a child. This diagnosis can be subjective, time consuming, inconclusive, does not provide precise insight into genetics and is not suitable for early detection. Machine Learning Methods can make relevant changes to speed up the process. It is known that early intervention is the key to improving children with autism. Obviously speeding up the diagnosis time is even more important in the case of Autism. Big data and machine learning technologies can make major advances in predicting and accelerating the complex and time-consuming process of diagnosis and treatment. Machine learning systems can be developed to take advantage of the vast amount of health and medical data available for predictive modeling and predictive analysis. In this paper, a comparison of several machine learning techniques and models will be tested and analyzed. In this study, it is proposed to apply the stacking technique to predict the presence of ASD. The results showed that the application of the stacking technique could improve the performance of the predictive model in ASD diagnosis.