TY - GEN
T1 - Optimal Decision Tree for Early Detection of Bipolar Disorder based on Crowdsourced Symptoms
AU - Paramita, Ni Luh Putu Satyaning P.
AU - Aqsari, Hasri Wiji
AU - Udiatami, Wilda Melia
AU - Sadewo, Ayu
AU - Yustisia, Whinda
AU - Cahyono, Dwy Bagus
AU - Jati, Putu Hadi Purnama
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Bipolar disorder is a chronic mental health disorder identified by periodic manic or depressive episodes. Early intervention for bipolar disorder is necessary to prevent progression and complications that lead to more societal loss. In this study, we build an early detection model for bipolar disorder based on crowdsourced mental health symptoms. The mental health symptoms are gathered through the crowdsourcing process in the form of free texts. The feature extraction is done using natural language processing techniques to convert free texts into binary features. Based on these features, we build an optimal decision tree model by formulating a mathematical optimization problem that minimizes misclassification loss and penalizes the number of leaves in the tree, constrained by a depth bound. The optimal decision tree model outperforms the baseline models in terms of accuracy (0.899), recall (0.869), precision (0.921), F1 score (0.894), and AUC (0.898). Moreover, the model is interpretable since it maintains the tree-like structure as in other decision tree models. This model can be used as an early detection tool to recommend for further examination of diagnosing bipolar disorder.
AB - Bipolar disorder is a chronic mental health disorder identified by periodic manic or depressive episodes. Early intervention for bipolar disorder is necessary to prevent progression and complications that lead to more societal loss. In this study, we build an early detection model for bipolar disorder based on crowdsourced mental health symptoms. The mental health symptoms are gathered through the crowdsourcing process in the form of free texts. The feature extraction is done using natural language processing techniques to convert free texts into binary features. Based on these features, we build an optimal decision tree model by formulating a mathematical optimization problem that minimizes misclassification loss and penalizes the number of leaves in the tree, constrained by a depth bound. The optimal decision tree model outperforms the baseline models in terms of accuracy (0.899), recall (0.869), precision (0.921), F1 score (0.894), and AUC (0.898). Moreover, the model is interpretable since it maintains the tree-like structure as in other decision tree models. This model can be used as an early detection tool to recommend for further examination of diagnosing bipolar disorder.
KW - bipolar disorder
KW - crowdsourcing
KW - interpretable machine learning
KW - natural language processing
KW - optimal decision tree
UR - http://www.scopus.com/inward/record.url?scp=85183470306&partnerID=8YFLogxK
U2 - 10.1109/ICIC60109.2023.10382060
DO - 10.1109/ICIC60109.2023.10382060
M3 - Conference contribution
AN - SCOPUS:85183470306
T3 - 2023 8th International Conference on Informatics and Computing, ICIC 2023
SP - 1
EP - 6
BT - 2023 8th International Conference on Informatics and Computing, ICIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Informatics and Computing, ICIC 2023
Y2 - 8 December 2023 through 9 December 2023
ER -