Table 8 A comparison of our proposed models with previous work.
From: Detection of autism spectrum disorder (ASD) in children and adults using machine learning
Refs | Dataset | Models | Accuracy | Limitations | Strengths |
|---|---|---|---|---|---|
Children 1054 instances along with 18 attributes | LR, SVM, KNN, RF | LR : 93.15% NB:94.79% SVM:93.84% KNN:90.52% RFC: 81.52% | The dataset was compiled from primarily autism-based collections, as a result of which there was quite a significant imbalance, in favor of the ASD class | Used forward feature selection Trained and tested five ML models | |
2009 features records (toddlers, children, adults) | SVM, Glmboost and adaboost classification | 85.10%, 97%, 98% | Constrained sample size/data set | Metrics based on brain activity used for prediction of ASD | |
Children have 292 instances | Naïve Bayes, SVM, LR, RF, CNN, NN | 97.53%, 9 6.30%, 96.88% | Does not predict the severity of ASD. Conditions used for identification of ASD which might not always necessarily translate to a case of ASD | Makes use of six different ML based classification methods and obtained high accuracy | |
Proposed models | Children Records: > 200 Attributes: 22 Adults Records: > 700 Attributes: 21 | SVM, LR | Children SVM: 99% LR: 98% Adults SVM: 81% LR: 78% | Size of children dataset is very small | Comparable accuracy has been obtained Two different datasets of children and adults have been processed State of the art FL technique has been applied for ASD prediction |
