Use of value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment

Abstract

The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual material increases, the sensitivity of the model for predicting cadet maladjustment using neural networks increases: 30.MLP 16-7-2; 28.MLP 16-13-2; 30.MLP 16-22-2; 29.MLP 16-31-2; 42.MLP 16-39-2; 19.MLP 16-45-2; 16.MLP 16-48-2; 30.MLP 16-30-2 from 0.43 to 1.00 conventional units (y = 0.017x² – 0.0647x + 0.4898, R² = 0.8264); specificity: from 0.96 to 1.00 conventional units (y = –0.002x² + 0.0211x + 0.9462, R² = 0.8923); predictive value increased from 91.8% to 99.45% (y = –0.1477x² + 2.3309x + + 90.238, R² = 0.9368). When the models were tested on new samples, the mean sensitivity was 0.45 conventional units with an increasing trend (y = 0.0207x² – 0.1214x + 0.5271, R² = 0,6945), specificity: 0.97 conventional units (y = –0.0048x² + + 0.0388x + 0.9086, R² = 0.772), predictive value: 92.6% (y = –0.4962x² + 3.5402x + 88.447, R² = 0.6598). Therefore, the model for predicting cadet maladjustment using neural networks can identify cadets who will experience maladjustment with an accuracy of 32% to 72%, whereas no more than 6% of cadets without maladjustment will receive a false prediction. The predictive value of the model is close to the absolute accuracy of vocational aptitude prediction with reference values of 65%–70%. The predictive ability of the models tested in the study, ranging from 89.7% to 96.4%, confirms the high effectiveness of using neural networks to predict maladjustment. The value-based and motivational parameters of the cadets, combined with the use of neural networks to predict their maladjustment, create a highly effective artificial intelligence system. Such an approach can be used in medical and psychological support activities for military personnel at a military university for their optimal selection and support.

Keywords

military personnel / prediction / maladjustment / neural network / artificial intelligence / mathematical modeling / value-based and motivational parameters / cadet

References

[1] Mosyagin IG. Role and place of maritime medicine. Marine Medicine. 2023;9(3):7–12. EDN: FJMQXK doi: 10.22328/2413-5747-2023-9-3-7-12

[2] Мосягин И.Г. Роль и место морской медицины // Морская медицина. 2023. Т. 9, № 3. С. 7–12. EDN: FJMQXK doi: 10.22328/2413-5747-2023-9-3-7-12

[3] Mosyagin IG. Role and place of maritime medicine. Marine Medicine. 2023;9(3):7–12. EDN: FJMQXK doi: 10.22328/2413-5747-2023-9-3-7-12

[4] Kryukov EV, Ivchenko EV, Yusupov VV, et al. Relationship of professional success and development of cognitive mental processes and personal qualities of Vietnam naval specialists. Bulletin of the Russian Military Medical Academy. 2024;26(1):7–14. EDN: GYBLZE doi: 10.17816/brmma624879

[5] Крюков Е.В., Ивченко Е.В., Юсупов В.В., и др. Взаимосвязь профессиональной успешности и развития познавательных психических процессов и личностных качеств специалистов Военно-морских сил Вьетнама // Вестник Российской военно-медицинской академии. 2024. Т. 26, № 1. C. 7–14. EDN: GYBLZE doi: 10.17816/brmma624879

[6] Kryukov EV, Ivchenko EV, Yusupov VV, et al. Relationship of professional success and development of cognitive mental processes and personal qualities of Vietnam naval specialists. Bulletin of the Russian Military Medical Academy. 2024;26(1):7–14. EDN: GYBLZE doi: 10.17816/brmma624879

[7] Andriychenko AM, Emushintsev PA. Application of objective diagnostic methods to assess professionally important qualities of divers. Marine Medicine. 2023;9(2):77–89. EDN: QPYTMV doi: 10.22328/2413-5747-2023-9-2-77-89

[8] Андрийченко А.М., Емушинцев П.А. Применение объективных методов диагностики для оценки профессионально важных качеств водолазов: ретроспективное исследование // Морская медицина. 2023. Т. 9, № 2. С. 77–89. EDN: QPYTMV doi: 10.22328/2413-5747-2023-9-2-77-89

[9] Andriychenko AM, Emushintsev PA. Application of objective diagnostic methods to assess professionally important qualities of divers. Marine Medicine. 2023;9(2):77–89. EDN: QPYTMV doi: 10.22328/2413-5747-2023-9-2-77-89

[10] Babin YuM, Yusupov VV, Blaginin AA, et al. Psychological and psychophysiological criteria for professional selection of aviation system operators. Military Medical Journal. 2024;345(8):43–50. EDN: EYPFFE

[11] Бабин Ю.М., Юсупов В.В., Благинин А.А., и др. Психологические и психофизиологические критерии профессионального отбора операторов авиационных систем // Военно-медицинский журнал. 2024. Т. 345. № 8. С. 43–50. EDN: EYPFFE

[12] Babin YuM, Yusupov VV, Blaginin AA, et al. Psychological and psychophysiological criteria for professional selection of aviation system operators. Military Medical Journal. 2024;345(8):43–50. EDN: EYPFFE

[13] Dorofeev II, Korzunin VA, Ovchinnikov BV, et al. Methodological aspects of allocation of categories of medical and psychological support of students. Clinical Psychology and Special Education. 2016;5(2):113–120. EDN: WGFEXT doi: 10.17759/cpse.2016050208

[14] Дорофеев И.И., Корзунин В.А., Овчинников Б.В., и др. Методологические аспекты выделения категории медико-психологического сопровождения курсантов // Клиническая и специальная психология. 2016. Т. 5, № 2. С. 113–120. EDN: WGFEXT doi: 10.17759/cpse.2016050208

[15] Dorofeev II, Korzunin VA, Ovchinnikov BV, et al. Methodological aspects of allocation of categories of medical and psychological support of students. Clinical Psychology and Special Education. 2016;5(2):113–120. EDN: WGFEXT doi: 10.17759/cpse.2016050208

[16] Ovchinnikov BV, Dnov KV, Zaitsev AG, et al. Values and motivation in the professionalization of the serviceman. Bulletin of the Russian Military Medical Academy. 2017;(4):135–139. EDN: YLNRQY

[17] Овчинников Б.В., Днов К.В., Зайцев А.Г., и др. Ценности и мотивация в профессионализации военнослужащего // Вестник Российской военно-медицинской академии. 2017. № 4. С. 135–139. EDN: YLNRQY

[18] Ovchinnikov BV, Dnov KV, Zaitsev AG, et al. Values and motivation in the professionalization of the serviceman. Bulletin of the Russian Military Medical Academy. 2017;(4):135–139. EDN: YLNRQY

[19] Kryukov EV, Ivchenko EV, Shamrey VK, et al. Modern approaches to the assessment of stress resistance in military personnel. Military Medical Journal. 2023;344(7):4–15. EDN: FNCQIP

[20] Крюков Е.В., Ивченко Е.В., Шамрей В.К., и др. Современные подходы к оценке стрессоустойчивости у военнослужащих // Военно-медицинский журнал. 2023. Т. 344, № 7. С. 4–15. EDN: FNCQIP

[21] Kryukov EV, Ivchenko EV, Shamrey VK, et al. Modern approaches to the assessment of stress resistance in military personnel. Military Medical Journal. 2023;344(7):4–15. EDN: FNCQIP

[22] Ivchenko EV, Ovchinnikov DV Organization of scientific work as a guarantee of successful development of military medicine. In: 3rd Asian-Pacific Congress on Military Medicine. Congress Proceedings. 2016. P. 24–25. (In Russ.)

[23] Ивченко Е.В., Овчинников Д.В. Организация научной работы как залог успешного развития военной медицины // 3-й Азиатско-тихоокеанский конгресс по военной медицине. Материалы конгресса. 2016. С. 24–25.

[24] Ivchenko EV, Ovchinnikov DV Organization of scientific work as a guarantee of successful development of military medicine. In: 3rd Asian-Pacific Congress on Military Medicine. Congress Proceedings. 2016. P. 24–25. (In Russ.)

[25] Lipsky DL, Gura MS, Luchkin IV, et al. Application of machine learning in the medical and psychological support of military service services of foreign states. Living Psychology. 2023;10(4):15–24. EDN: PQSOJZ doi: 10.58551/24136522_2023_10_4_15

[26] Липский Д.Л., Гура М.С., Лучкин И.В., и др. Применение машинного обучения в медико-психологическом сопровождении военнослужащих иностранных государств // Живая психология. 2023. Т. 10, № 4. С. 15–24. EDN: PQSOJZ doi: 10.58551/24136522_2023_10_4_15

[27] Lipsky DL, Gura MS, Luchkin IV, et al. Application of machine learning in the medical and psychological support of military service services of foreign states. Living Psychology. 2023;10(4):15–24. EDN: PQSOJZ doi: 10.58551/24136522_2023_10_4_15

[28] Soshkin PA. distress tolerance and adaptive capabilities in naval specialists with signs of professional burnout. Marine Medicine. 2021;7(3): 62–70. EDN: RZUUGE doi: 10.22328/2413-5747-2021-7-3-62-70

[29] Сошкин П.А. Стрессоустойчивость и адаптивные возможности у военно-морских специалистов с признаками профессионального выгорания // Морская медицина. 2021. Т. 7, № 3. С. 62–70. EDN: RZUUGE doi: 10.22328/2413-5747-2021-7-3-62-70

[30] Soshkin PA. distress tolerance and adaptive capabilities in naval specialists with signs of professional burnout. Marine Medicine. 2021;7(3): 62–70. EDN: RZUUGE doi: 10.22328/2413-5747-2021-7-3-62-70

[31] Lichko A.E. Psychopathy and character accentuations in adolescents. Leningrad: Medicine; 1983. 255 p. (In Russ.)

[32] Личко А.Е. Психопатии и акцентуации характера у подростков. Ленинград: Медицина, 1983. 255 с.

[33] Lichko A.E. Psychopathy and character accentuations in adolescents. Leningrad: Medicine; 1983. 255 p. (In Russ.)

[34] Yusupov VV, Fishchenko DE, Yatmanov AN, et al. Machine learning in building cadet maladaptation forecasts. Medicо-Biological and Socio-Psychological Problems of Safety in Emergency Situations. 2023;(4):90–96. EDN: REWQVC doi: 10.25016/2541-7487-2023-0-4-90-96

[35] Юсупов В.В., Фищенко Д.Е., Ятманов А.Н., и др. Прогноз дезадаптации курсантов с применением методов машинного обучения // Медико-биологические и социально-психологические проблемы безопасности в чрезвычайных ситуациях. 2023. № 4. С. 90–96. EDN: REWQVC doi: 10.25016/2541-7487-2023-0-4-90-96

[36] Yusupov VV, Fishchenko DE, Yatmanov AN, et al. Machine learning in building cadet maladaptation forecasts. Medicо-Biological and Socio-Psychological Problems of Safety in Emergency Situations. 2023;(4):90–96. EDN: REWQVC doi: 10.25016/2541-7487-2023-0-4-90-96

[37] Baskin YuG, Svidzinskaya GB. Experience of using the semantic differential method in the educational process. Bulletin of the St. Petersburg University of the State Fire Service of the Ministry of Emergency Situations of Russia. 2021;(1):158–165. EDN: ZPNLEZ

[38] Баскин Ю.Г., Свидзинская Г.Б. Опыт использования метода семантического дифференциала в образовательном процессе // Вестник Санкт-Петербургского университета Государственной противопожарной службы МЧС России. 2021. № 1. С. 158–165. EDN: ZPNLEZ

[39] Baskin YuG, Svidzinskaya GB. Experience of using the semantic differential method in the educational process. Bulletin of the St. Petersburg University of the State Fire Service of the Ministry of Emergency Situations of Russia. 2021;(1):158–165. EDN: ZPNLEZ

[40] Borisov DN, Koluzov AV, Serezhkin IA. Opportunities for the development of artificial intelligence and big data in the field of health of servicemen In: State and prospects of development of modern science in the direction of “ASU, information and telecommunication systems”. 2020;3:177–183. EDN: XCNRHN

[41] Борисов Д.Н., Колузов А.В., Сережкин И.А. Возможности развития искусственного интеллекта и больших данных в области здоровья военнослужащих // Состояние и перспективы развития современной науки по направлению «АСУ, информационно-телекоммуникационные системы». 2020. Т. 3. С. 177–183. EDN: XCNRHN

[42] Borisov DN, Koluzov AV, Serezhkin IA. Opportunities for the development of artificial intelligence and big data in the field of health of servicemen In: State and prospects of development of modern science in the direction of “ASU, information and telecommunication systems”. 2020;3:177–183. EDN: XCNRHN

[43] Terletskiy AS, Terletskaya ES. Neural Networks and Artificial Intelligence: Foundations of Neural Networks in Python. Lipetsk; 2023. 76 p. (In Russ.) EDN: UGIPEE

[44] Терлецкий А.С., Терлецкая Е.С. Нейронные сети и искусственный интеллект: основы нейронных сетей на языке python. Липецк, 2023. 76 с. EDN: UGIPEE

[45] Terletskiy AS, Terletskaya ES. Neural Networks and Artificial Intelligence: Foundations of Neural Networks in Python. Lipetsk; 2023. 76 p. (In Russ.) EDN: UGIPEE

[46] Apchel Vya, Bulka AP, Sysoev VN. Use of a mathematical model of a fuzzy neural network for professional psychological selection of personnel. Academic Journal. 2007;(5):136–143. (In Russ.) EDN: XWYOHB

[47] Апчел В.Я., Булка А.П., Сысоев В.Н. Использование математической модели нечёткой нейронной сети для профессионального психологического отбора кадров // Академический журнал. 2007. № 5. С. 136–143. EDN: XWYOHB

[48] Apchel Vya, Bulka AP, Sysoev VN. Use of a mathematical model of a fuzzy neural network for professional psychological selection of personnel. Academic Journal. 2007;(5):136–143. (In Russ.) EDN: XWYOHB

[49] Zaitsev AG, Rezvantsev MV, Tegza VYu, et al. Mathematical model of forecast successfulness of N.G. Kuznetsov naval academy cadets’ professional military adaptation. Bulletin of the Russian Military Medical Academy. 2018;(1):160–163. EDN: YXCBUD

[50] Зайцев А.Г., Резванцев М.В., Тегза В.Ю., и др. Математическая модель прогноза успешности военно-профессиональной адаптации курсантов военно-морской академии им. Н.Г. Кузнецова // Вестник Российской военно-медицинской академии. 2018. № 1. С. 160–163. EDN: YXCBUD

[51] Zaitsev AG, Rezvantsev MV, Tegza VYu, et al. Mathematical model of forecast successfulness of N.G. Kuznetsov naval academy cadets’ professional military adaptation. Bulletin of the Russian Military Medical Academy. 2018;(1):160–163. EDN: YXCBUD

[52] Yatmanov AN. Psychological features of cadets with military professional maladjustment. Bulletin of the Russian Military Medical Academy. 2019;(2):92–96. (In Russ.) EDN: KFFMDW

[53] Ятманов А.Н. Психологические особенности профессионально дезадаптированных курсантов // Вестник Российской военно-медицинской академии. 2019. № 2. С. 92–96. EDN: KFFMDW

[54] Yatmanov AN. Psychological features of cadets with military professional maladjustment. Bulletin of the Russian Military Medical Academy. 2019;(2):92–96. (In Russ.) EDN: KFFMDW

RIGHTS & PERMISSIONS

Eco-Vector