# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 4 Conclusions  
This study is an in-depth analysis providing a deeper understanding of NDs with the development of a precise and reliable predictive model, using a logistic regression algorithm to forecast the presence of NDs.  
The phases of this study's analysis to achieve accurate and meaningful results were as follows: (i) data preprocessing for data consistency and homogeneity; (ii) cluster analysis for grouping similar observations into homogeneous clusters; (iii) reliability analysis to assess the consistency and reliability of our variables' measurements; (iv) factor analysis for the identification of latent factors that align with the respective clusters highlighted among reliable and consistent variables, forming a more robust model; and (v) a predictive model based on machine learning techniques using the logistic regression model, through which we predicted the presence of an ND based on the clusters identified in the cluster analysis.  
The results of this study are comparable with two previous experiments on the Smart-Speech game score dataset [19; 45]. This study's prediction model has better accuracy, 81.52%, than the best performing method of those studies, which was the Grammatical Evolution variant named GenClass, with an accuracy of 79.56%. Moreover, the precision and recall metrics are superior in this study's prediction model.  
The findings of this study have given us a prediction model that is more accurate and clinically relevant. This model can help clinicians with the early diagnosis and treatment of NDs by providing valuable insights. This study encourages further research to focus on finding new ways to make models that help children with NDs deal with real-life problems more effectively. These models should be easy to understand, flexible, and more helpful.  
Conceptualization, E.I.T. and J.P.; methodology, E.I.T. and J.P.; data curation, V.S. and J.P.; writing--original draft preparation, E.I.T., V.S. and I.G.T.; writing--review and editing, E.I.T., I.G.T. and J.P.; visualization, E.I.T. and V.S.; supervision, E.I.T.; project administration, E.I.T.; funding acquisition, E.I.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project titled \"Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficiencies Indicators in Child Communication\", with code HP1 AB-28185 (MIS: 5033088), supported by the European Regional Development Fund (ERDF).  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the University of Ioannina, Greece, (protocol code 18435 on 15 May 2020).  
Informed Consent Statement: Informed written consent was obtained from all participating parents, after informing them regarding the study's compliance with GDPR regulations.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available as participants of this study did not give written consent for their data to be shared publicly, so due to privacy restrictions and the sensitive nature of this research, data sharing is not applicable to this article.  
We wish to thank all the participants for their valuable contribution in this study as well as administrative and technical support.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.