Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology
This paper presents a procedure that aims to combine explanatory and predictive modeling for the construction of new psychometric questionnaires based on psychological and neuroscientific theoretical grounding. It presents the methodology and the results of a procedure for items selection that consi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-03-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyg.2020.00446/full |
| _version_ | 1831813298718244864 |
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| author | Pasquale Dolce Davide Marocco Mauro Nelson Maldonato Raffaele Sperandeo |
| author_facet | Pasquale Dolce Davide Marocco Mauro Nelson Maldonato Raffaele Sperandeo |
| author_sort | Pasquale Dolce |
| collection | DOAJ |
| description | This paper presents a procedure that aims to combine explanatory and predictive modeling for the construction of new psychometric questionnaires based on psychological and neuroscientific theoretical grounding. It presents the methodology and the results of a procedure for items selection that considers both the explanatory power of the theory and the predictive power of modern computational techniques, namely exploratory data analysis for investigating the dimensional structure and artificial neural networks (ANNs) for predicting the psychopathological diagnosis of clinical subjects. Such blending allows deriving theoretical insights on the characteristics of the items selected and their conformity with the theoretical framework of reference. At the same time, it permits the selection of those items that have the most relevance in terms of prediction by therefore considering the relationship of the items with the actual psychopathological diagnosis. Such approach helps to construct a diagnostic tool that both conforms with the theory and with the individual characteristics of the population at hand, by providing insights on the power of the scale in precisely identifying out-of-sample pathological subjects. The proposed procedure is based on a sequence of steps that allows the construction of an ANN capable of predicting the diagnosis of a group of subjects based on their item responses to a questionnaire and subsequently automatically selects the most predictive items by preserving the factorial structure of the scale. Results show that the machine learning procedure selected a set of items that drastically improved the prediction accuracy of the model (167 items reached a prediction accuracy of 88.5%, that is 25.6% of incorrectly classified), compared to the predictions obtained using all the original items (260 items with a prediction accuracy of 74.4%). At the same time, it reduced the redundancy of the items and eliminated those with less consistency. |
| first_indexed | 2024-12-22T21:52:12Z |
| format | Article |
| id | doaj.art-4caecf81b8c04ace91b59b2a77384d33 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-22T21:52:12Z |
| publishDate | 2020-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-4caecf81b8c04ace91b59b2a77384d332022-12-21T18:11:21ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-03-011110.3389/fpsyg.2020.00446506199Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and PhenomenologyPasquale Dolce0Davide Marocco1Mauro Nelson Maldonato2Raffaele Sperandeo3Department of Public Health, University of Naples Federico II, Naples, ItalyDepartment of Humanistic Studies, University of Naples Federico II, Naples, ItalyDepartment of Neuroscience and Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, ItalySiPGI Postgraduate School in Gestalt Integrated Psychotherapy, Torre Annunziata, ItalyThis paper presents a procedure that aims to combine explanatory and predictive modeling for the construction of new psychometric questionnaires based on psychological and neuroscientific theoretical grounding. It presents the methodology and the results of a procedure for items selection that considers both the explanatory power of the theory and the predictive power of modern computational techniques, namely exploratory data analysis for investigating the dimensional structure and artificial neural networks (ANNs) for predicting the psychopathological diagnosis of clinical subjects. Such blending allows deriving theoretical insights on the characteristics of the items selected and their conformity with the theoretical framework of reference. At the same time, it permits the selection of those items that have the most relevance in terms of prediction by therefore considering the relationship of the items with the actual psychopathological diagnosis. Such approach helps to construct a diagnostic tool that both conforms with the theory and with the individual characteristics of the population at hand, by providing insights on the power of the scale in precisely identifying out-of-sample pathological subjects. The proposed procedure is based on a sequence of steps that allows the construction of an ANN capable of predicting the diagnosis of a group of subjects based on their item responses to a questionnaire and subsequently automatically selects the most predictive items by preserving the factorial structure of the scale. Results show that the machine learning procedure selected a set of items that drastically improved the prediction accuracy of the model (167 items reached a prediction accuracy of 88.5%, that is 25.6% of incorrectly classified), compared to the predictions obtained using all the original items (260 items with a prediction accuracy of 74.4%). At the same time, it reduced the redundancy of the items and eliminated those with less consistency.https://www.frontiersin.org/article/10.3389/fpsyg.2020.00446/fullmachine learningpredictive modelingexplanatory modelingitem selectionneural networkspsychopathological assessment |
| spellingShingle | Pasquale Dolce Davide Marocco Mauro Nelson Maldonato Raffaele Sperandeo Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology Frontiers in Psychology machine learning predictive modeling explanatory modeling item selection neural networks psychopathological assessment |
| title | Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology |
| title_full | Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology |
| title_fullStr | Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology |
| title_full_unstemmed | Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology |
| title_short | Toward a Machine Learning Predictive-Oriented Approach to Complement Explanatory Modeling. An Application for Evaluating Psychopathological Traits Based on Affective Neurosciences and Phenomenology |
| title_sort | toward a machine learning predictive oriented approach to complement explanatory modeling an application for evaluating psychopathological traits based on affective neurosciences and phenomenology |
| topic | machine learning predictive modeling explanatory modeling item selection neural networks psychopathological assessment |
| url | https://www.frontiersin.org/article/10.3389/fpsyg.2020.00446/full |
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