Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping
Abstract
:1. Introduction
2. Literature Review
Related Works
3. Methodology
- N is the number of instances in a dataset with M features.
- xih = the instance of the same class as xi (nearest hit neighbor);
- xim = the instance of a different class (nearest miss neighbor);
- δ(xij, xih) = the difference between the feature j values of xi and its nearest hit neighbor xih; and
- δ(xij, xim) measures the difference between the feature j values of xi and its nearest miss neighbor xim.
4. Experimental Analysis
5. Conclusions, Limitations, and Ethical Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods Used | Data Used | Performance | Reference |
---|---|---|---|
DLiB library Deep learning Kaldi toolkit | 125 toddlers | Sensitivity: 80.00% Specificity: 69.80% Accuracy: 74.80%. | [10] |
LR RF | IBM MarketScan Health Claims database; 38,576 observations | RF:
| [11] |
CNN | Dataset: collected by ASDTests 6075 observations |
| [12] |
SOM RF NB | 2000 observations | NB:
| [13] |
C4.5 RIPPER RF NB | ASDTest dataset; 1054 toddler data observations | No data sampling: NB: Sensitivity: 96.20% C4.5: Sensitivity: 92.30% RIPPER: Sensitivity: 92.40% RF: Sensitivity: 95.30% | [15] |
Symmetrical uncertainty (SU), IG, fast-correlated-based filter (FCBF), leave one out cross-validation (LOOCV), gini index, and chi-square ID3 ADABoost Knn | ASDTest dataset; 1054 toddler data observations | No data sampling. Sensitivity rates between 93% and 98%, depending on the feature sets used by the classification algorithm. The highest sensitivity rate was achieved by ADABoost. | [18] |
NB with data sampling: SMOTE RUS | ASDTest dataset; 1118 adult data observations | SMOTE+ NB:
| [22] |
mRMR and chi-square testing feature selection C4.5 RIPPER RF NB SVM | ASDTest dataset; 1054 toddler data observations | No data sampling. Sensitivity rates between 93% and 97.50%, depending on the feature sets used by the classification algorithm. The highest Sensitivity rate was achieved by the SVM. | [17] |
NB with data sampling: SMOTE ROS RUS | ASDTest dataset; over 1000 observations | SMOTE + NB:
| [14] |
kNN, SVM RF | ASD dataset of UCI machine-learning data repository | kNN: Accuracy: 95.70% Sensitivity: 95.15% F-measure: 94.64% AUC: 96.00% SVM: Accuracy: 99.90% Sensitivity: 99.90% F-measure: 99.90% AUC: 100% RF: Accuracy: 99.90% Sensitivity: 99.90% F-measure: 99.90% AUC: 99.90% | [16] |
Multilayer perceptron (MLP) classifier | Social responsiveness scale (SRS) [28] - child/adolescent questionnaire; 16,527 children/adolescents | AUC: 92.00% | [27] |
SVM CNN ANN | An integrated data from the UCI machine-learning data repository, consisting of three datasets with 20 common attributes | CNN algorithm.
| [29] |
Multivariate LR MLP RF | EMR data from a single Israeli health maintenance organization; 96,138 EMR children information | Multivariate LR:
| [30] |
Question Number | Question Details | Corresponding DSM-5 |
---|---|---|
Q1 | Does your child look at you when you call his/her name? | Deficits in social communication and interaction (problem with social initiation and response) |
Q2 | How easy is it for you to have eye contact with your child? | Deficits in social communication and interaction (non-verbal communication problem) |
Q3 | Does your child point to indicate that s/he wants something (e.g., a toy that is out of reach)? | Deficits in joint attention and social communication and interaction (non-verbal communication problem) |
Q4 | Does your child point to share interest with you (e.g., pointing at an interesting sight)? | Deficits in joint attention and social communication and interaction (non-verbal-communication problems) |
Q5 | Does your child pretend (e.g., care for dolls, talk on a toy phone)? | Deficits in social communication and interaction related to pretend play |
Q6 | Does your child follow where you are looking? | Deficits in joint attention and social communication and interaction (non-verbal communication problems) |
Q7 | If you or someone else in the family is visibly upset, does your child show signs of wanting to comfort them (e.g., stroking their hair, hugging them)? | Deficits in social communication and interaction (problems with social initiation and response) |
Q8 | Would you describe your child’s first words as (Very typical, Quite typical, Slightly unusual, Very unusual, My child doesn’t speak) | Deficits in social communication and interaction related to language development. Stereotyped/repetitive speech |
Q9 | Does your child use simple gestures (e.g., wave goodbye)? | Deficits in social communication and interaction (non-verbal communication problem) |
Q10 | Does your child stare at nothing with no apparent purpose? | Shows restricted and repetitive patterns of behavior, interests, or activities (stereotyped behaviors) |
Attribute Rank | Gain Ratio Score | Attribute Rank | Pearson Correlation Score | Attribute Rank | Relief Score |
---|---|---|---|---|---|
Q6 | 0.281297 | Q6 | 0.5978 | Q6 | 0.30551 |
Q9 | 0.263325 | Q9 | 0.5653 | Q5 | 0.30501 |
Q5 | 0.240884 | Q5 | 0.5492 | Q8 | 0.28446 |
Q4 | 0.222894 | Q4 | 0.5229 | Q9 | 0.28521 |
Q3 | 0.222216 | Q8 | 0.5163 | Q4 | 0.24612 |
Q8 | 0.20949 | Q7 | 0.4805 | Q3 | 0.20902 |
Q7 | 0.182863 | Q3 | 0.4783 | Q2 | 0.19799 |
Q2 | 0.163217 | Q2 | 0.461 | Q7 | 0.19098 |
Q1 | 0.146544 | Q1 | 0.4181 | Q1 | 0.17945 |
FamilyASDHistory | 0.013727 | FamilyASDHistory | 0.1316 | Q10 | 0.05514 |
Ethnicity | 0.006148 | Age | 0.1201 | Ethnicity | 0.02957 |
Jaundice | 0.006557 | Jaundice | 0.0903 | FamilyASDHistory | 0.02331 |
User | 0.005728 | User | 0.0802 | User | 0.01805 |
Q10 | 0.000547 | Q10 | 0.0272 | Age | 0.00827 |
Sex | 0.000169 | Ethnicity | 0.0252 | Jaundice | 0.001 |
Age | 0 | Sex | 0.0137 | Sex | -0.00802 |
Set 1: No Feature Selection | Set 2: Q1 to Q9 | Set 3: Highest Scoring attributes | Set 4: Secondary/Lowest Scoring Attributes |
---|---|---|---|
Q1 (Communication) | Q1 | Q6 | FamilyASDHistory |
Q2 (Social interaction) | Q2 | Q9 | Age |
Q3 (Communication) | Q3 | Q5 | Q10 |
Q4 (Social interaction) | Q4 | Q8 | User |
Q5 (Social interaction) | Q5 | Q4 | Jaundice |
Q6 (Social interaction) | Q6 | Ethnicity | |
Q7 (Social interaction) | Q7 | Sex | |
Q8 (Communication) | Q8 | ||
Q9 (Social interaction) | Q9 | ||
Q10(Repetitive patterns) | |||
Age | |||
Sex | |||
Ethnicity | |||
Jaundice | |||
Family ASD history | |||
User |
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Abdelhamid, N.; Thind, R.; Mohammad, H.; Thabtah, F. Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping. Bioengineering 2023, 10, 1131. https://doi.org/10.3390/bioengineering10101131
Abdelhamid N, Thind R, Mohammad H, Thabtah F. Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping. Bioengineering. 2023; 10(10):1131. https://doi.org/10.3390/bioengineering10101131
Chicago/Turabian StyleAbdelhamid, Neda, Rajdeep Thind, Heba Mohammad, and Fadi Thabtah. 2023. "Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping" Bioengineering 10, no. 10: 1131. https://doi.org/10.3390/bioengineering10101131