首页|Southern Illinois University Edwardsville Researcher Details Research in Machine Learning (Childhood Environmental Instabilities and Their Behavioral Implicatio ns: A Machine Learning Approach to Studying Adverse Childhood Experiences)
Southern Illinois University Edwardsville Researcher Details Research in Machine Learning (Childhood Environmental Instabilities and Their Behavioral Implicatio ns: A Machine Learning Approach to Studying Adverse Childhood Experiences)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Edwardsville, Illinois, by NewsRx editors, the research stated, "Adverse childhood experiences (ACEs) inclu de a range of abusive, neglectful, and dysfunctional household behaviors that ar e strongly associated with long-term health problems, mental health conditions, and societal difficulties." Financial supporters for this research include National Science Foundation. The news journalists obtained a quote from the research from Southern Illinois U niversity Edwardsville: "The study aims to uncover significant factors influenci ng ACEs in children aged 0-17 years and to propose a predictive model that can b e used to forecast the likelihood of ACEs in children. Machine learning models a re applied to identify and analyze the relationships between several predictors and the occurrence of ACEs. Key performance metrics such as AUC, F1 score, recal l, and precision are used to evaluate the predictive strength of different facto rs on ACEs. Family structures, especially non-traditional forms such as single p arenting, and the frequency of relocating to a new address are determined as key predictors of ACEs. The final model, a neural network, achieved an AUC of 0.788 , a precision score of 0.683, and a recall of 0.707, indicating its effectivenes s in accurately identifying ACE cases. The model's ROC and PR curves showed a hi gh true positive rate for detecting children with two or more ACEs while also po inting to difficulties in classifying single ACE instances accurately."
Southern Illinois University Edwardsvill eEdwardsvilleIllinoisUnited StatesNorth and Central AmericaCyborgsEm erging TechnologiesMachine Learning
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