CAN WE PREDICT BEHAVIOR PROBLEMS IN CHILDREN WITH AUTISM?

Kids with autism often “see” the world differently than other kids do. They can have unique experiences of vision, hearing, taste, smell, or touch sensations. These sensory changes are often linked to behavior problems such as isolation, lack of interest, aggression, anxiety, depression, or lack of attention. We thought it would helpful if we could detect behavior problems that might not be obvious yet but are possible in the future. In our study, we used computer programs, based on a type of artiﬁcial intelligence called machine learning, to predict possible behavior problems based on how autistic

kids receive sensations in their everyday lifes.Our programs analyze the answers to test questions about the way kids perceive the world through their senses, and these programs can then make reliable predictions of behavior problems before they arise.These early predictions allow families and doctors to be aware of and treat those problems early.

WHAT IS AUTISM?
Autism is a brain disorder with a big impact on how people perceive AUTISM A disorder a ecting communication and behavior, often characterized by challenges with social interactions, repetitive behaviors, and unique strengths and di erences in how a person perceives and interacts with the world.the world around them, how they behave, and how they socialize (for more information on autism, see this Frontiers for Young Minds article).Autistic kids may struggle with social interactions like making friends, for example.They can also have unusual interests, like trains, dinosaurs, tra c lights...The behavior of autistic kids can cause problems in their development, growth and social relationships.
Kids with autism often perceive visual, auditory (hearing), taste, smell, or touch sensations di erently than kids without autism do.For example, overreaction to certain daily life sounds as the fridge, a fan...These di erences in perception are believed to be related to the behavioral problems that these kids often experience.What if we could predict behavior changes before they are obvious and cause problems, just by understanding the way that kids are perceiving sensations?This knowledge could give doctors an early sign of behavior changes so they could treat them, and could also give families time to learn how to manage behavior changes.
To know how kids perceive their environments (i.e., how they see, hear, taste, touch, or smell) is not simple, but parents of autistic kids can take some tests that provide valuable information for doctors and researchers.We used two tests for kids and teenagers: a sensory test, which asks about kids' perceptions, and a test about behavior problems such as anxiety, rule breaking, or attention problems [ , ].In our research, we asked parents to complete the sensory test, and then we used the scores from that test to predict the scores on the behavior test.

OUR WORK
During the study, we asked the parents of autistic kids ( girls and boys) to take the sensory and behavior tests.Kids' ages ranged from to years old.The sensory test, called Sensory Profile (SP-), has questions [ ], and the answers are scores with possible values of (not applicable), (almost never), (occasionally), (half of the time), (frequently), or (almost always).
The questions in the SP-test are divided into groups: seeking, avoiding, sensitivity, and registration.The scores in these groups define how the child "sees" the world.For example, high scores in questions from the seeking group mean that the child wants to feel everything from the environment, like touching everything, watching bright lights very closely, or taking risks climbing on a tree.High avoiding scores mean that the child does not want to feel sensations around themself and prefers to avoid them.High sensitivity scores mean that the child feels sensations stronger than other people do.
High scores in the registration category mean that the child barely feels sensations that others easily feel.Extremely high or low values on any of the questions indicate sensory experiences that are di erent from non-autistic kids.Figure A shows the number of questions for each group, with example questions.We thought that sensations of touch (touch processing) could also be interesting to predict changes in behavior.behavioral problems that we looked at in our study, with examples of questions.

MACHINE LEARNING
We wanted to predict whether kids with autism are likely to have problematic behavior changes based on how they "see" their environments.To predict behavior problems that are not happening yet might sound complex, but thanks to computer programs based on machine learning it has become much easier.Machine learning

MACHINE LEARNING
A type of artificial intelligence that allows computers to learn from data and improve their performance over time.
programs are used, for example, in sports, streaming platforms, or social media, to make content suggestions based on what we have watched or liked.These programs make predictions about future situations (for example, a picture we might like) by learning from information about the past (other pictures we liked, accounts we follow, etc.).In our case, we wanted to see if we could use machine learning to predict a child's scores on the CBCL test based only on the scores from their SP-test.
Let us imagine the scenario in Figure .There is a -year-old girl who kicks a ball, and it goes m.Another girl, who is years old, kicks the ball m.Can you try to guess how far a -year-old girl might be able to kick the ball?Maybe it is not easy to know for sure, but common sense tells you that the ball will most likely travel a distance between and m, because the -year-old girl is expected to be stronger than the -year-old girl but weaker than the -year-old girl.

Figure Figure
Predictions can be made from existing information.For example, if you know that a -year-old girl can kick a ball m, and a -year-old girl can kick a ball m, you can make a prediction of how far a -year-old girl might be able to kick a ball.
In this example, the age of the girl is used to predict the distance the ball travels.In our autism study, the machine learning program uses the scores from the SP-test to predict future changes in behavior, which would be reflected in a future CBCL test.For example, in

Training
To train the program, we gave it examples from kids' scores on SPand CBCL tests.For example, we gave the program a child's scores from the SP-group avoiding (question scores , , , , , , , , , , , , , , , , , , , ) and the child's social problems score ( ) in the CBCL test; a second child has di erent avoiding scores ( , , , , , , , , , , , , , , , , , , , ) and a social problems score of ; etc.We gave the computer this kind of data for out of kids, thus leaving one kid out.These data compose the training set, as

TRAINING SET
A data set used to teach an algorithm.It has input and output pairs that the algorithm learns from.
it is meant to teach the computer program the relationship between SP-and CBCL scores-similar to how you could see a relationship between age and how far the ball was kicked in Figure .Testing Once the computer program was trained, we gave it SP-scores from the child who was not included in the training stage.Using the relationships learned from the training data, the program calculates the CBCL score using just the child's SP-scores.The training and testing were repeated times, each one leaving a di erent child out of the training set and using it for testing.

Reliability Assessment
To see if the program's predictions were accurate, we needed the true CBCL scores for the kids whose data were used in the testing stage.We analyzed how similar the predicted and true CBCL scores were.The closer the predicted scores were to the real scores, the more reliable the computer's prediction.
There are several machine learning programs that can be used to get our prediction.Their reliability may change depending on the groups of SP-scores and the types of CBCL scores to be predicted.In our research, we tried machine learning programs [ ], and we made di erent programs for each of the groups included in the SP-test.The use of machine learning programs with the combination of the SP-groups and the CBCL scores means that we tried × × = , di erent programs.

RESULTS
We saw that the most reliable program accurately predicts the external behavior problems in CBCL considering all the SP-scores (total group in Figure A).The scores predicted by our program di er by just unit with respect to the true value.Here is an example to make this clear.
We the SP-test to a child.The total group that the child scores on the SP-test is the collection of the scores between and .We give these numbers to the program, which makes mathematical calculations and predicts a value of for the external problems score.Then, we apply the CBCL test to the child, and they get an external problems score of .Comparing the predicted ( ) and true ( ) scores, we see that the program was very close to the true score-only one unit o .
Other programs also achieved reliable predictions for the following eight behavior problems: anxious/depressed, withdrawn/depressed, social problems, thought problems, attention problems, aggressive behavior, internal, and total.The prediction is less reliable for the remaining two problems: physical complaints and rule-breaking behavior.Specifically, we learned that avoiding has a significant influence on anxious/depressed.Seeking has a strong influence on attention problems and physical complaints.Touch processing plays a role in rule breaking behavior.Finally, registration influences social problems.
Considering the high reliability in the prediction of these nine types of behavioral problems, it may eventually be unnecessary to use the CBCL test at all-we could possibly just use the SP-test and predict the CBCL scores from those results.If the predicted CBCL score indicates that there are behavioral problems, doctors can begin to treat those problems.

CONCLUSION
Autistic kids often receive sensations from their environments in unusual ways, and this may lead to behavior problems.To understand this connection better, we used two tests: one that assess alterations in the senses, and one that looks at behavior problems.By analyzing scores from the sensory test, we created machine learning programs that could predict scores on the behavior test.We found that prediction of external behavior problems (those that all people can see) is highly reliable using the total of all the sensory scores.Our programs can also accurately predict eight other behavior problems, but they are not as good at predicting the remaining two.These findings show that it is possible to anticipate problems in behavior by looking at early signs of sensory alterations.Doctors can then use this information to provide early treatments that may help to reduce future behavior problems.

CONFLICT OF INTEREST:
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figure
Figure

Figure
Figure (A) The SP-test is designed to understand how an autistic child "sees" the world.The questions are divided into five groups based on the type of sensory perception being looked at.Scores for each question range from (not applicable) to (almost always).(B) The CBCL test looks at behavior problems.Scores for each question range from (sometimes true) to (very true).
Figure we used information on how a child avoids sensory experiences Tubío-Fungueiriño et al. (avoiding in Figure A) to predict the existence of problems in social environments (social problems in Figure B), as we will see in the next section.

FigureFigure
Figure , Fernández-Delgado, Cernadas, Carracedo and Fernández-Prieto.This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with these terms.YOUNG REVIEWERS CALEB, AGE: Hello, my name is Caleb.I live in Nevada.I play basketball and my favorite team is the Golden State Warriors and I love to try new things and hobbies.