Autism Classification Based On Logistic Regression Model

Autistic Spectrum Disorder (ASD) is a developmental disability can affect communication and behavior. Existing research has shown that early diagnosis can help doctors to find this disease early and can save significant healthcare costs. With the rapid growth of ASD cases, a ASD related dataset created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis. This article developed and test an Autism classification algorithm which based on logistic regression model. The result of this study provided a model can predict the ADS in an average F1 score of 0.97, which displays the superiority of proposed model. Besides, the data visualization part displays several feature distribution images for people to better understand the data and related feature engineering.