1. FCM WIZARD IN ACTION SCENARIO: AUTISM PREDICTION IN CHILDREN Main developers: Gonzalo NÁPOLES and Isel GRAU Supervisors: Elpiniki PAPAGEORGIOU and Koen VANHOOF

2. Scenario description This scenario deals with a Fuzzy Cognitive Map (FCM) for modeling and predicting autistic spectrum disorder. A decision system based on human knowledge and experience with a FCM trained using unsupervised non-linear hebbian learning algorithm is discussed. This model serves as a guide in determining the prognosis and in planning the appropriate therapies to children. A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292. The proposed FCM comprises 24 concepts, while three experts (a pediatrician, an occupational therapist and a special educator) were used to determine key concepts of the model as well as their interconnections among detected concepts. This knowledge engineering process was completed through a questionnaire, which was created for this process and proposed to the team of experts in the autism field.

3. ConceptDescriptionConceptDescription C1Enjoy being swungC13Imitate C2Take an interest in other childrenC14Response to the name C3Climbing on thingsC15Looking at a toy when pointing C4Enjoy playingC16Walking C5Pretend other thingsC17Look at things you are looking at C6Pointing index fingerC18Unusual finger movements near his/her face C7Indication of interestC19Attract your attention C8Playing with small toysC20Deafness C9Bringing objects to parentsC21Understanding what others say C10Eye contactC22Stare at nothing C11Oversensitive to noiseC23Look at your face to check reaction C12Smile in response to parents faceOUTAutism (High, Probable Autism and No autism) A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

4. Construction of the causal weights Experts describe each interconnections with linguistic fuzzy rule to assign the weight. The fuzzy rules for each interconnection can be calculated using the following: A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

5. Create a new map by using the menu File |New Map. The canvas is immediately cleaned and ready to be used in a new map. Designing the FCM model A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

6. Designing the FCM model Add concepts and causal links between them by drag and dropping from the source concept to the target one. Concepts denote variables and OUT denotes the autism class. During the modeling phase the designer write the value of each causal weight. A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

7. Once all concepts and relations have been determined, it is necessary to customize concepts and define the ranges for the classes using the cut-off for the decision node. Designing the FCM model A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

8. Adaptation of causal weights A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

9. The tool includes unsupervised and supervised learning algorithms. Set up the parameters for the chosen algorithm and browse for the historical dataset source (in ARFF format). Adaptation of causal weights In this study case we prefer to use an unsupervised learning approach for adapting the parameters since the initial modeling must be considered. However, using an evolutionary approach we cannot ensure small variations of the initial modeling and therefore the system could be difficult to explain. A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

10. To adjust the matrix for the first scenario (Definite Autism) we use an activation vector designed by experts, corresponding to the desired decision class. Adaptation of causal weights Final response for the activation vector A1 = {0.3, 0.55, 0.6, 0.65, 0.2, 0.69, 0.73, 0.77, 0.86, 0.1, 0.57, 0.4, 0.50, 0.62, 0.6, 0.71, 0.9, 0.15, 0.25 0.45, 0.49, 0.34, 0.62, 0.51} corresponding to “definite autism” Likewise we can adjust the causal relations for classes Autism and No Autism. A. KANNAPPAN, A. TAMILARASI B, E. PAPAGEORGIOU, ANALYZING THE PERFORMANCE OF FUZZY COGNITIVE MAPS WITH NON-LINEAR HEBBIAN LEARNING ALGORITHM IN PREDICTING AUTISTIC DISORDER, EXPERT SYSTEMS WITH APPLICATIONS 38 (2012) 1282–1292.

11. FCM WIZARD IN ACTION SCENARIO: AUTISM PREDICTION IN CHILDREN Main developers: Gonzalo NÁPOLES and Isel GRAU Supervisors: Elpiniki PAPAGEORGIOU and Koen VANHOOF