000			03487nam1a2200457 4500
001			663748
005			20231030041905.0
035			_a(RuTPU)RU\TPU\network\34918
035			_aRU\TPU\network\34868
090			_a663748
100			_a20210303a2020 k y0engy50 ba
101	0		_aeng
102			_aUS
135			_adrcn ---uucaa
181		0	_ai
182		0	_ab
200	1		_aRealizing a Stacking Generalization Model to Improve the Prediction Accuracy of Major Depressive Disorder in Adults _fN. Mahendran, D. R. Vincent, K. Srinivasan [et al.]
203			_aText _celectronic
300			_aTitle screen
320			_a[References: 47 tit.]
330			_aMajor depressive disorder (MDD) is a persistent psychiatric mood disorder that is prevalent from a few weeks to a few months, even for years in the worst cases. It causes sadness, hopelessness in the individuals; sometimes, it forces them to hurt themselves. In severe cases, MDD can even lead to the death of the individual. It is challenging to diagnose MDD as it co-occurs with many other disorders (Co-Morbid) and many other reasons such as mobility, lack of motivation, and cost. The way to diagnose MDD is usually high ended that is challenging for the regular clinicians to diagnose. Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model. The data collected has been cleaned with a preprocessing technique, and feature selection are performed using wrapper based methods; moreover, in the final step, a stacking based ensemble learning model is implemented to classify the MDD patients. Furthermore, KNN Imputation is implemented for preprocessing, Random Forest-Based Backward Elimination for feature selection and multi-layer perceptron, SVM and Random Forest as low-level learners in stacking generalization model. The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers.
333			_aРежим доступа: по договору с организацией-держателем ресурса
461			_tIEEE Access
463			_tVol. 8 _v[P. 49509-49522] _d2020
610	1		_aтруды учёных ТПУ
610	1		_aэлектронный ресурс
610	1		_aK-nearest neighbors
610	1		_amajor depressive disorder
610	1		_amultilayer perceptron
610	1		_arandom forest
610	1		_arandom forest-based feature elimination
610	1		_astacking generalization and support vector machine
701		1	_aMahendran _bN. _gNivedhitha
701		1	_aVincent _bD. R. _gDurai Raj
701		1	_aSrinivasan _bK. _gKathiravan
701		1	_aSharma _bV. _gVishal
701		1	_aDzhayakodi (Jayakody) Arachshiladzh _bD. N. K. _cspecialist in the field of electronics _cProfessor of Tomsk Polytechnic University _f1983- _gDushanta Nalin Kumara _2stltpush _3(RuTPU)RU\TPU\pers\37962
712	0	2	_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bНаучно-образовательный центр "Автоматизация и информационные технологии" _h8422 _2stltpush _3(RuTPU)RU\TPU\col\27515
801		2	_aRU _b63413507 _c20210303 _gRCR
856	4	0	_uhttps://doi.org/10.1109/ACCESS.2020.2977887
942			_cBK