# Early Stage Prediction Model of Autism Spectrum Disorder Traits of Toddlers

Mousumi Bala, Mohammad Hanif Ali

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## To cite this version:

Mousumi Bala, Mohammad Hanif Ali. Early Stage Prediction Model of Autism Spectrum Disorder Traits of Toddlers. 7th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Jan 2023, Chennai, India. pp.3-17, 10.1007/978-3-031-39811-7_1. hal-05363251

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# Early Stage Prediction Model of Autism Spectrum Disorder Traits of Toddlers

Mousumi Bala¹(✉) and Mohammad Hanif Ali²

¹ Eastern University, Savar, Bangladesh
moubalacse@gmail.com

² Jahanginagar University, Savar, Bangladesh
hanif_ju03@juniv.edu

**Abstract.** Globally, autism spectrum disorder (ASD) is a major concern. It is a complex neurological condition that affects a child's ability to identify objects, faces, express emotions, and develop social skills. It is incurable, but earlier detection and diagnosis of ASD aid in better treatment. It begins in early childhood, although symptoms may be recognized as late as adulthood. As a result, many children are unable to get proper treatment at an early age, causing their health to become more complicated. In our study, we explored the toddler dataset. The selection of the important features in medical applications such as ASD screening therefore plays a key role in model development, since that directly affects classification accuracy. This study examines several feature selection approaches. We use Generalized Learning Vector Quantization (GLVQ) and propose a method, Modified Generalized Learning Vector Quantization (MGLVQ) for selecting important features. We apply knearest neighbor (kNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Multilayer Perceptron (MLP) classification machine learning algorithms. The performance of classifiers is evaluated by evaluation methods such as accuracy, $F\beta$ and area under the curve (AUROC). The results of all classifiers have been examined, and the performance of the MLP in different evaluation methods is excellent. Moreover, we illustrate the MGLVQ technique explores important features that impact the primary diagnostic procedure of ASD traits in toddlers. As a result, the proposed machine learning model could be effective in detecting ASD at an earlier stage. The objective of our study is to build an online application that can accurately identify ASD at an early stage using machine learning.

**Keywords:** ASD · toddlers · feature selection · classifiers · MLP

## 1 Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that impacts how a person makes interaction and communication with others [1]. The actual cause of ASD is still unknown. Autistic people show different abnormal activities like repetitive behaviors, restricted interests, social communication deficits, etc.
About 1 in 100 children are estimated as ASD throughout the world [3]. Like other autistic patients, it causes significant and serious developmental delays in toddlers. Hence, it is essential to detect ASD of them in the preliminary stage [4]. To assess ASD in toddlers [5], it is required to identify toddler's behavioral history from their parents, relatives and caregivers. Likewise, a question-based survey is useful where different conditions are placed by researchers. The toddlers whose covered in these conditions may have ASD. Therefore, various autism screening tools including the checklist for ASD in toddlers (Q-CHAT) [6], MCHAT, AQ, etc. are used for such symptoms to manipulate this condition [7].

<figure><img src="image_5.png" /><figcaption>Fig. 1. The proposed framework of predicting ASD traits of toddlers.</figcaption></figure>

In this study, we developed a machine-learning model that predicts ASD in toddlers. However, different features of toddlers were identified using Generalized Learning Vector Quantization (GLVQ) and Modified Generalized Learning Vector Quantization (MGLVQ) methods for screening autism more precisely. Then, various classifiers including k-nearest neighbor (kNN), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Classifier (SVM), and Multilayer Perceptron (MLP) were employed in the individual feature subsets. For identifying the best model for the dataset. Various evaluation metrics were used to compare the outcomes of each classifier.

Due to social harassment, many individuals in developing countries (i.e., particularly in Bangladesh) are not agreed to check for autism physically, hence they refuse to go to health professionals. The implication of this study is considered that anyone can use and determine autism in toddlers using this model. Further, this study assists us in better understanding the traits of toddlers and early detect autism and ensuring the treatment policy for them.

The contributions of this study are given as follows:

- Proposed model is especially detected ASD in toddlers.

- We identified several discriminatory factors of ASD for toddlers.
- Proposed methodology outperforms the results of several standard classifiers and accompanying pre-trained models.

- Our proposed method MGLVQ performs better than other methods.

## 2 Related Work

Islam et al. [9] used machine learning methods to detect ASD optimum behaviors. The toddler data set collected from the Kaggle repository [8] consisted of twenty features and one target feature with binary class. They applied SVM, Random forest, Naive Bayes and kNN classifiers. They observed that kNN and Random Forest showed the highest accuracy at 98% and 93%.

Mohanty et al. [10] illustrated the deep learning model using ASD data sets (toddlers, child, adolescent and adult). They were considered principal component analysis (PCA) for the reduction of feature dimension and used deep neural network (DNN). They found accuracy as 85.24% for toddlers, 84.24% for adolescent, 85.71% for child and 89.24% for adult data sets. Saihi et al. [11] developed the machine learning model for toddler data set. They applied three classifiers such as C4.5, random forest, and neural network and found that the neural network showed excellent accuracy at 99%.

Akyol et al. [12] developed the fuzzy rule model to identify the most prevalent features used for ASD detection in children up to the age of three years. In order to identify the most useful features from the dataset, they developed a hybrid model of logistic regression and fuzzy rules methods. The findings indicated that feature selection had a significant effect on predicting autistic traits in individuals with ASD. Alzubi et al. [13] suggested a hybrid feature selection technique that combines the logical mutual information maximization and SVM recursive feature elimination methods. According to the findings, the hybrid technique outscored other algorithms by up to 89%. The authors have shown, from that results, that the subset of the dataset produced with their hybrid model is enough, at least on the basis of the dataset, for an efficient distinction between those affected and those healthy.

Rajab et al. [14] proposed a machine learning model and they applied different feature selection methods. The classifiers kNN, AdaBoost, and ID3 were applied for the toddler dataset and they observed that the maximum accuracy was 98.34% of AdaBoost with fast correlated-based filter. Satu et al. [15] used Autism Barta apps for collecting data and applied different classifiers. They showed that J48 performed the best outcome than other classifiers.

We develop an efficient model for ASD prediction using toddlers autism screening datasets provided by Fadi Fayez Thabtah [8].

## 3 Methodology

As shown in Fig. 1, the proposed model consists of several steps which are given as follows.
<table><caption>**Table 1.** Variable mapping of toddlers dataset</caption><thead><tr><td>Variable</td><td>Q-CHAT-10 Features (18–36 months)</td></tr></thead><tbody><tr><td>Answer Code 1</td><td>Does your child look at you when you call his/her name?</td></tr><tr><td>Answer Code 2</td><td>How easy is it for you to get eye contact with your child?</td></tr><tr><td>Answer Code 3</td><td>Does your child point to indicate that s/he wants something?</td></tr><tr><td>Answer Code 10</td><td>Does your child stare at nothing with no apparent purpose?</td></tr></tbody></table>

<table><caption>**Table 2.** Feature description of toddlers dataset</caption><thead><tr><td>Serial no</td><td>Feature</td><td>Data type</td><td>Description</td></tr></thead><tbody><tr><td>1–10</td><td>A1–A10</td><td>Binary</td><td>The answer code of the corresponding question</td></tr><tr><td>11</td><td>Age Mons</td><td>Integer</td><td>Toddlers (months)</td></tr><tr><td>12</td><td>Gender</td><td>String(f, m)</td><td>Male or female</td></tr><tr><td>13</td><td>Score</td><td>Integer</td><td>Based on the screening test answers, score out of 10</td></tr><tr><td>14</td><td>Ethnicity</td><td>String</td><td>List of common ethnicities</td></tr><tr><td>15</td><td>Jaundice</td><td>String(f, m)</td><td>Whether the toddler was born with jaundice</td></tr><tr><td>16</td><td>Family ASD</td><td>String(yes, no)</td><td>If the toddler’s family were diagnosed with autism</td></tr><tr><td>17</td><td>User</td><td>String</td><td>Who is responding to the screening test questions</td></tr><tr><td>18</td><td>Class ASD</td><td>String(Yes, No)</td><td>ASD traits or No ASD traits of toddler</td></tr></tbody></table>

## 3.1 Data Preprocessing

In this study, we collected toddlers data set from Kaggle data repository [8]. It includes both autistic and non-autistic toddlers records whose ages are from 12 to 36 months. It follows a standard screening tool called Q-Chat-10 [9] to collect cases. The toddlers data set contains 1054 instances with 18 features where 30.76 percent of toddlers are female and 69.24 percent are male. This survey was taken from toddler’s parent, caregiver, and medical personnel. Tables 1 and 2 show a description of the relevant features of the Toddler data set. In the pre-processing steps, we cleaned this data set and removed several unwanted things. Then, we performed one hot encoding technique to convert categorical features into binary values.

## 3.2 Feature Selection

In this work, we used two methods as Generalized Learning Vector Quantization (GLVQ) and Modified Generalized Learning Vector Quantization (MGLVQ) to identify significant features of ASD. [17].

**Generalized Learning Vector Quantization.** As a supervised learning approach, GLVQ is best suited for classification tasks [16]. It is a method for enhancing vector
quantization-based techniques, particularly learning vector quantization (LVQ) 2.1[17]. The algorithm's flow is:

1. The input space has two reference vectors, r1 and r2.

2. It is possible to define an input vector y in such a way that r1 is the closest reference vector that also belongs to the same class as y, and r2 is the closest reference vector that instead belongs to a class that is different from y.

3. Using the Euclidean distance, r1 and r2 that are relatively close to the input vector. To define the relative distance difference $\mu(y)$, we will use the following formula:

$$ \mu(y) = \frac{(l_1 - l_2)}{l_1 + l_2} \qquad (1) $$

where $l_1$ and $l_2$ are the distances of y from r1 and r2. $\mu(y)$ ranges between -1 and +1. If the, is negative, y is classified correctly. So we need to decrease $\mu(y)$, value.

4. Using the following equation, the minimization of cost function K is determined:

$$ K = \sum_{j=1}^{n} f(\mu(y_j)) \qquad (2) $$

where n is used for training of input vectors and the function $\mu(y)$ is monotonically rising.

5. The following is an updated version of the reference vectors:

$$ r_1 = r_1 + \alpha \frac{\delta f}{\delta \theta} \frac{l_2}{(l_1 + l_2)^2} (y - r_1) \qquad (3) $$

$$ r_2 = r_2 + \alpha \frac{\delta f}{\delta \theta} \frac{l_1}{(l_1 + l_2)^2} (y - r_1) \qquad (4) $$

where $\alpha$ is a learning rate.

**Proposed Method.** We proposed a new concept of GLVQ classifier, named Modified Generalized Learning Vector Quantization (MGLVQ). The flow of the algorithm is as follows:

1. The input space has two reference vectors, r1 and r2.

2. It is possible to define an input vector y in such a way that r1 is the closest reference vector that also belongs to the same class as y, and r2 is the closest reference vector that instead belongs to a class that is different from y.

3. Using the Euclidean distance, r1 and r2 that are relatively close to the input vector. To define the relative distance difference $\mu(y)$, we will use the following formula:

$$ \mu(y) = \frac{(l_1 - l_2)}{(\xi l_1 + l_2)} \qquad (5) $$

where $l_1$ and $l_2$ are the distances of y from r1 and r2. Where $\xi = 4$, is a key factor $\mu(y)$ ranges between -1 and +1. If the $\mu(y)$, is negative, y is classified correctly. So we need to decrease $\mu(y)$ value.
4. Using the following equation, the minimization of cost function K is determined:

$$K = \sum_{j=1}^{n} f(\mu(y_j)) \qquad (6)$$

where n is used for training of input vectors and the function μ(y) is monotonically rising.

5. The following is an updated version of the reference vectors:

$$r_1 = r_2 + \alpha \frac{\delta f}{\delta \theta} \frac{5l_2}{(4l_1 + l_2)^4} (y - r_1) \qquad (7)$$

$$r_2 = r_2 + \alpha \frac{\delta f}{\delta \theta} \frac{5l_1}{(4l_1 + l_2)^4} (y - r_1) \qquad (8)$$

where α is a learning rate.

## 3.3 Classification

In this work, we used several popular classification methods including k-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Multilayer perceptron (MLP).

<figure><img src="image_6.png" /><figcaption>Fig. 2. Comparison between GLVQ and MGLVQ.</figcaption></figure>

## 3.4 Performance Metrics

The performance metrics including accuracy [18], Fβ [20], and the area under the curve (AUROC) [14] were used to investigate the performance of individual classifiers which are represented as follows:

True Positive (TrPo) values The toddlers with ASD appropriately identified as having ASD. True Negative (TrNe): The individual did not have ASD and was appropriately diagnosed as such. False Positive (FaPo): When an individual who does not have ASD is wrongly labeled as having ASD. False Negative (FaNe): A individual with ASD was
wrongly labeled as not having ASD. True positive rate(TPR) and false positive rate(FPR) are considered for measuring the average area under the ROC for all possible points.

$$ Accuracy = \frac{(T_r P_o + T_r N_e)}{(T_r P_o + F_a P_o + F_a N_e + T_r N_e)} \qquad (9) $$

$$ F_{\beta} = (1 + \beta^2) * \frac{(precision * recall)}{((\beta^2 * precision) + recall)} \qquad (10) $$

$$ TRP = \frac{T_r P_o}{(T_r P_o + F_a N_e)} \qquad (11) $$

$$ FRP = \frac{F_a P_o}{(F_a P_o + T_r N_e)} \qquad (12) $$

$$ AUROC = \frac{TRP - FRP + 1}{2} \qquad (13) $$

where TrPo, TrNe, FaPo, FaNe are called true positive, true negative, false positive, and false negative values respectively.

<table><caption>**Table 3.** The observations with various values of $\beta$</caption><thead><tr><th>Observations</th><th>Training (%):Testing (%)</th></tr></thead><tbody><tr><td>Observation 1 (β = 50)</td><td>50:50</td></tr><tr><td>Observation 2 (β = 60)</td><td>60:40</td></tr><tr><td>Observation 3 (β = 70)</td><td>70:30</td></tr><tr><td>Observation 4 (β = 80)</td><td>80:20</td></tr><tr><td>Observation 5 (β = 90)</td><td>90:10</td></tr></tbody></table>

## 4 Experimental Result

We used sci-kit machine learning libraries for each of the classifiers separately for these methods. We applied the Generalized Learning Vector Quantization (GLVQ) and Modified Generalized Learning Vector Quantization (MGLVQ) methods to the toddlers data set for selecting important features. The comparison of the above two methods for feature selection was shown in Fig. 2. We considered only the positive value of coefficients for both methods. The subset (A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Gender, Jaundice) was selected by the Generalized Learning Vector Quantization (GLVQ) and denoted as FS GLVQ. The subset (A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, Age, Gender, Jaundice, Ethnicity) was also selected by the Modified Generalized Learning Vector Quantization (MGLVQ) and denoted as FS MGLVQ. With 18 features, the data set of toddlers was denoted as FS full. On these subsets, KNN, LDA, CART, NB, SVM, and MLP were applied and determined accuracy, $F_{\beta}$, and AUROC of them.
Therefore, the performance of these classifiers is needed to investigate. In this case, we considered five observations with different $\beta$ values (i.e., such as 50, 60, 70, 80, 90) and split this data set where 50%, 60%, 70%, 80% and 90% of the data set were used for training and 50%, 40%, 30%, 20% and 10% were used for testing purpose. Table 3 shows the observations for the training data and the testing data with different values of $\beta$. The training and testing data was chosen at random 100 times for each case of $\beta$. The accuracy and AUROC of the individual classifiers are shown in Table 4 and Table 6 for all subsets. Also, Table 5 shows the score of $F\beta$ of all classifiers with all subsets.

<table><caption>**Table 4.** Accuracy for various classifiers.</caption><thead><tr><th>Observations</th><th>Classifier</th><th>FS full</th><th>FS GLVQ</th><th>FS MGLVQ</th></tr></thead><tbody><tr><td rowspan="6">Observation 1</td><td>KNN</td><td>92.42</td><td>93.74</td><td>94.31</td></tr><tr><td>LDA</td><td>93.84</td><td>94.69</td><td>94.12</td></tr><tr><td>CART</td><td>88.24</td><td>90.51</td><td>90.70</td></tr><tr><td>NB</td><td>90.51</td><td>92.60</td><td>93.93</td></tr><tr><td>SVM</td><td>96.21</td><td>96.58</td><td>96.39</td></tr><tr><td>MLP</td><td>98.86</td><td>99.05</td><td>100.00</td></tr><tr><td rowspan="6">Observation 2</td><td>KNN</td><td>92.18</td><td>94.08</td><td>94.79</td></tr><tr><td>LDA</td><td>96.92</td><td>94.79</td><td>96.45</td></tr><tr><td>CART</td><td>91.47</td><td>92.18</td><td>91.47</td></tr><tr><td>NB</td><td>93.84</td><td>93.13</td><td>95.97</td></tr><tr><td>SVM</td><td>97.16</td><td>98.10</td><td>96.45</td></tr><tr><td>MLP</td><td>99.76</td><td>100.00</td><td>100.00</td></tr><tr><td rowspan="6">Observation 3</td><td>KNN</td><td>92.43</td><td>94.34</td><td>93.06</td></tr><tr><td>LDA</td><td>95.90</td><td>95.58</td><td>94.95</td></tr><tr><td>CART</td><td>90.85</td><td>91.17</td><td>90.22</td></tr><tr><td>NB</td><td>95.27</td><td>97.16</td><td>95.90</td></tr><tr><td>SVM</td><td>97.79</td><td>98.11</td><td>98.42</td></tr><tr><td>MLP</td><td>99.68</td><td>100.00</td><td>100.00</td></tr><tr><td rowspan="4">Observation 4</td><td>KNN</td><td>91.94</td><td>94.31</td><td>95.73</td></tr><tr><td>LDA</td><td>92.89</td><td>93.84</td><td>96.21</td></tr><tr><td>CART</td><td>92.88</td><td>93.84</td><td>94.42</td></tr><tr><td>NB</td><td>95.26</td><td>94.79</td><td>95.73</td></tr></tbody></table>

(continued)
<table><caption>**Table 4.** (continued)</caption><thead><tr><th>Observations</th><th>Classifier</th><th>FS full</th><th>FS GLVQ</th><th>FS MGLVQ</th></tr></thead><tbody><tr><td rowspan="8">Observation 5</td><td>SVM</td><td>97.16</td><td>98.58</td><td>99.53</td></tr><tr><td>MLP</td><td>99.53</td><td>100.00</td><td>100.00</td></tr><tr><td>KNN</td><td>91.94</td><td>97.17</td><td>91.51</td></tr><tr><td>LDA</td><td>92.89</td><td>96.23</td><td>93.40</td></tr><tr><td>CART</td><td>91.00</td><td>92.45</td><td>90.57</td></tr><tr><td>NB</td><td>95.26</td><td>94.34</td><td>92.45</td></tr><tr><td>SVM</td><td>97.16</td><td>99.00</td><td>90.57</td></tr><tr><td>MLP</td><td>99.53</td><td>100.00</td><td>100.00</td></tr></tbody></table>

**Result Analysis of KNN:** We considered all observations where KNN showed the high accuracy 95.73% when $\beta = 80$ from other observations for subset FS MGLVQ. The maximum score of $F\beta$ was 98.26% in observation-1 for subset FS GLVQ. Then, KNN gave the high score of AUROC was 99.64% for subset FS GLVQ in observation-5.

**Result Analysis of LDA:** In observation-4, LDA showed the maximum accuracy 98.45% from other observations for subset FS MGLVQ. It obtained the high score 96.21% for $F\beta$ for subset FS MGLVQ in observation-4. It showed high score of AUROC was 99.54% for observation-3 with subset FS full.

**Result Analysis of CART:** In observation-4, CART obtained the high accuracy 93.84% from other observations for subset FS GLVQ. The high score of $F\beta$ was 96.75% for observation-4 with subset FS MGLVQ. It required maximum score of AUROC was 92.50% for observation-5 with subset FS MGLVQ.

**Result Analysis of Naive Bayes:** For observation-3, the Naive Bayes showed a high accuracy of 97.16% from other observations for subset FS GLVQ. The maximum score of $F\beta$ was 97.81% for observation-3 with subset FS GLVQ.

It required a high score of AUROC 99.26% for observation-2 with subset FS GLVQ.

**Result Analysis of SVM:** The SVM achieved a maximum accuracy of 99.53% in the observations-4 for the FS MGLVQ subset. When using the subset FS MGLVQ, the maximum $F\beta$ score was 99.45%. With subset FS GLVQ, AUROC for observation-4 was 99.92%.

**Result Analysis of MLP:** The MLP achieved maximum accuracy of all observations from other classifiers. It showed the accuracy range from 98.86% to 99.76% with the values of $\beta$ for FS full. The accuracy ranged from 99.05% to 100% for subset FS GLVQ and 100% for FS MGLVQ with all observations of MLP. It acquired the score 100% when $\beta \ge 60\%$ for FS GLVQ. It obtained the maximum accuracy of 100% with subset FS MGLVQ for all test observations. The MLP acquired the score of $F\beta$ range from 97.45% to 99.44% for FS full, from 97.81% to 99.90% for FS GLVQ and from 92.65% to 99.46% for FS MGLVQ. The MLP achieved a maximum score $F\beta$ of 99.90% for
FS GLVQ. It also obtained the score of AUROC range from 97.98% to 99.97% for FS full, from 99.52% to 100% for FS GLVQ and from 99.59% to 100% for FS MGLVQ. It achieved the high score AUROC of 100% from other classifiers for FS MGLVQ.

Figure 3 showed graphs of the AUROC curves for toddlers dataset for subsets of FS MGLVQ of the case of $\beta = 80\%$. We observed that the MLP achieved the best AUROC result from others.

<table><caption>**Table 5.** F$\beta$ for various classifiers.</caption><thead><tr><th>Observations</th><th>Classifier</th><th>FS full</th><th>FS GLVQ</th><th>FS MGLVQ</th></tr></thead><tbody><tr><td rowspan="6">Observation 1</td><td>KNN</td><td>97.13</td><td>96.54</td><td>96.67</td></tr><tr><td>LDA</td><td>97.22</td><td>97.67</td><td>96.76</td></tr><tr><td>CART</td><td>90.64</td><td>93.58</td><td>93.77</td></tr><tr><td>NB</td><td>92.47</td><td>94.49</td><td>95.23</td></tr><tr><td>SVM</td><td>97.98</td><td>97.80</td><td>96.46</td></tr><tr><td>MLP</td><td>97.78</td><td>97.81</td><td>96.47</td></tr><tr><td rowspan="6">Observation 2</td><td>KNN</td><td>97.37</td><td>96.15</td><td>96.32</td></tr><tr><td>LDA</td><td>98.46</td><td>97.16</td><td>97.26</td></tr><tr><td>CART</td><td>93.06</td><td>94.55</td><td>94.01</td></tr><tr><td>NB</td><td>95.54</td><td>94.57</td><td>95.96</td></tr><tr><td>SVM</td><td>97.37</td><td>98.23</td><td>96.12</td></tr><tr><td>MLP</td><td>97.77</td><td>98.26</td><td>96.13</td></tr><tr><td rowspan="6">Observation 3</td><td>KNN</td><td>95.12</td><td>97.00</td><td>96.74</td></tr><tr><td>LDA</td><td>97.31</td><td>97.31</td><td>97.37</td></tr><tr><td>CART</td><td>93.45</td><td>94.90</td><td>93.75</td></tr><tr><td>NB</td><td>96.36</td><td>97.81</td><td>97.43</td></tr><tr><td>SVM</td><td>97.44</td><td>98.63</td><td>98.98</td></tr><tr><td>MLP</td><td>97.45</td><td>98.64</td><td>98.99</td></tr><tr><td rowspan="6">Observation 4</td><td>KNN</td><td>97.08</td><td>95.62</td><td>97.51</td></tr><tr><td>LDA</td><td>96.61</td><td>96.24</td><td>98.45</td></tr><tr><td>CART</td><td>94.14</td><td>94.91</td><td>96.34</td></tr><tr><td>NB</td><td>96.28</td><td>95.79</td><td>96.75</td></tr><tr><td>SVM</td><td>97.98</td><td>98.69</td><td>99.45</td></tr><tr><td>MLP</td><td>97.99</td><td>98.99</td><td>99.46</td></tr></tbody></table>

(continued)
<table><caption>**Table 5.** (continued)</caption><thead><tr><th>Observations</th><th>Classifier</th><th>FS full</th><th>FS GLVQ</th><th>FS MGLVQ</th></tr></thead><tbody><tr><td rowspan="6">Observation 5</td><td>KNN</td><td>97.08</td><td>98.26</td><td>94.51</td></tr><tr><td>LDA</td><td>96.61</td><td>98.80</td><td>96.03</td></tr><tr><td>CART</td><td>94.79</td><td>94.67</td><td>90.12</td></tr><tr><td>NB</td><td>96.28</td><td>94.97</td><td>93.39</td></tr><tr><td>SVM</td><td>97.98</td><td>99.89</td><td>92.64</td></tr><tr><td>MLP</td><td>99.44</td><td>99.90</td><td>92.65</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** AUCROC curve of the MGLVQ for the observation of $\beta = 80\%$.</figcaption></figure>

<table><caption>**Table 6.** AUROC for various classifiers</caption><thead><tr><th>Observations</th><th>Classifier</th><th>FS full</th><th>FS GLVQ</th><th>FS MGLVQ</th></tr></thead><tbody><tr><td rowspan="6">Observation 1</td><td>KNN</td><td>96.71</td><td>98.82</td><td>99.18</td></tr><tr><td>LDA</td><td>98.46</td><td>99.35</td><td>99.14</td></tr><tr><td>CART</td><td>83.58</td><td>89.68</td><td>90.61</td></tr><tr><td>NB</td><td>98.67</td><td>98.94</td><td>99.08</td></tr><tr><td>SVM</td><td>98.23</td><td>99.83</td><td>99.92</td></tr><tr><td>MLP</td><td>99.00</td><td>100.00</td><td>100.00</td></tr><tr><td rowspan="2">Observation 2</td><td>KNN</td><td>98.35</td><td>99.10</td><td>98.54</td></tr><tr><td>LDA</td><td>99.18</td><td>99.20</td><td>99.24</td></tr></tbody></table>

(continued)
<table><caption>**Table 6. (continued)**</caption><thead><tr><th>Observations</th><th>Classifier</th><th>FS full</th><th>FS GLVQ</th><th>FS MGLVQ</th></tr></thead><tbody><tr><td rowspan="4"></td><td>CART</td><td>87.25</td><td>89.64</td><td>89.76</td></tr><tr><td>NB</td><td>94.55</td><td>99.24</td><td>98.83</td></tr><tr><td>SVM</td><td>99.68</td><td>99.84</td><td>99.70</td></tr><tr><td>MLP</td><td>99.97</td><td>100.00</td><td>99.92</td></tr><tr><td rowspan="7">Observation 3</td><td>KNN</td><td>98.22</td><td>98.55</td><td>97.78</td></tr><tr><td>LDA</td><td>99.54</td><td>99.05</td><td>99.30</td></tr><tr><td>CART</td><td>81.55</td><td>87.92</td><td>90.07</td></tr><tr><td>NB</td><td>98.34</td><td>99.22</td><td>99.12</td></tr><tr><td>SVM</td><td>99.89</td><td>99.61</td><td>99.21</td></tr><tr><td>MLP</td><td>99.90</td><td>100.00</td><td>99.59</td></tr><tr><td rowspan="7">Observation 4</td><td>KNN</td><td>95.64</td><td>97.51</td><td>97.21</td></tr><tr><td>LDA</td><td>97.49</td><td>97.92</td><td>98.79</td></tr><tr><td>CART</td><td>80.11</td><td>85.54</td><td>86.00</td></tr><tr><td>NB</td><td>95.11</td><td>98.18</td><td>98.02</td></tr><tr><td>SVM</td><td>98.01</td><td>98.63</td><td>99.23</td></tr><tr><td>MLP</td><td>99.44</td><td>99.89</td><td>100.00</td></tr><tr><td rowspan="7">Observation 5</td><td>KNN</td><td>95.54</td><td>99.64</td><td>97.44</td></tr><tr><td>LDA</td><td>97.49</td><td>98.69</td><td>97.02</td></tr><tr><td>CART</td><td>85.66</td><td>92.50</td><td>87.14</td></tr><tr><td>NB</td><td>95.11</td><td>99.28</td><td>99.64</td></tr><tr><td>SVM</td><td>98.05</td><td>98.80</td><td>99.23</td></tr><tr><td>MLP</td><td>97.98</td><td>99.52</td><td>100.00</td></tr></tbody></table>

# 5 Discussion

In this study, we implemented the toddler dataset for ASD prediction. To begin with, we needed to acquire a satisfactory result using a dataset we already had to do some essential pre-processing on for a machine learning framework. To perform properly, the learning model must be well-trained. Additionally, we should do some good data pre-processing in preparation for future proofreading in the learning model, which refers to the acceptability of models and their proper performance. This is also dependent on pre-processing. Thus, What we did during pre-processing was to retain all of the feature values except for three feature values, in binary. Among those three features, “Ethnicity” was one of them, and it was a string type. Regarding the strings, We encoded the data using a technique called “one-hot encoding.” It converts data of the string type to a binary value, however unique. We applied GLVQ and MGLVQ for feature selection and implemented KNN, LDA, CART, NB, SVM, and MLP classifiers for the dataset. The
<table><caption>**Table 7.** Comparison of our model to the recent studies.</caption><thead><tr><th>Dataset</th><th>Feature reduction</th><th>Paper</th><th>Accuracy (%)</th><th>Fβ (%)</th><th>AUROC (%)</th></tr></thead><tbody><tr><td rowspan="4">Toddler</td><td>Yes</td><td>[14]</td><td>98.34</td><td>—</td><td>99.62</td></tr><tr><td>No</td><td>[19]</td><td>98.77</td><td>—</td><td>99.98</td></tr><tr><td>No</td><td>[18]</td><td>100</td><td>—</td><td>—</td></tr><tr><td>Yes</td><td>Proposed model</td><td>100</td><td>99.90</td><td>100</td></tr></tbody></table>

outcomes of all these algorithms were analyzed and observed that MLP showed good performance from others. The accuracy of MLP for the toddlers dataset was obtained as 100% with the subset of proposed MGLVQ methods. Using the MLP classification approach, we can accurately identify ASD traits in toddlers.

We evaluated by comparing our model to the recent literature reviews, as indicated in Table 7. Rajab et al. [14] employed the toddlers dataset for the prediction ASD and implemented kNN, AdaBoost, and ID3 classifiers. In their observation, the highest results for AdaBoost as 98.34% accuracy and 99.62% AUROC were found. Akter et al. [19] found 98.77% accuracy and 99.98% AUROC for the toddlers dataset. Hossain et al. [18] observed the results of accuracy 100% for toddlers dataset.

# 6 Conclusion

We investigated the proposed MGLVQ method picked out important features and performed better than others. We tested the performance of several classifiers on different subsets and observed the MLP classification did significantly better than the others. Diagnostic procedures for ASD are rarely inexpensive. We discovered that detecting ASD traits in toddlers at an early stage can help with diagnosis. Using our proposed model, ASD traits in toddlers can be easily diagnosed. ASD diagnosis is a time-consuming procedure. If it can be identified early on, it can save money and time. Because we don't have enough data to train our model, it has only one limitation. The absence of a dataset is the most likely explanation for the restrictions on the design or technique that affected or influenced our thesis study. It didn't assist us at all, even after we preprocessed the data. Autism spectrum disorders (ASDs) have traditionally been detected at various ages, but we've focused our efforts on detecting ASDs early on. To acquire the most accurate findings possible, we decided to restrict our model to a certain age range. The aim of having such an age limit in our model is to achieve the most accurate results achievable.

We can do a lot more with more accurate findings and more data to develop our model. Most countries struggle to recognize autism as early as possible, but with our model and the collection of questions we gathered, the issue may be easily solved. In the future, we will need to examine a large amount of data and try to replace traditional classifiers with deep learning. However, we are considering developing a user-friendly smartphone app for end users based on our suggested model, so that any person may use the application to detect early ASD signs easily and find medical help if necessary.
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