Classifying Bipolar Personality Disorder (BPD) Using Long Short-Term Memory (LSTM)

Ghosh, Sreyan; Pahuja, Kunj; Jiji, Joshua Mammen; Puthussery, Antony; Shukla, Samiksha; Unal, Aynur

doi:10.1007/978-981-15-5309-7_17

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model is validated using the Alan Turing Institute synthesized signature database within pattern psychiatric repository. The end outcome has an AUC of 0.95, which is higher than the previous result of 0.90 [ 18 ].…”

Section: Related Workcontrasting

confidence: 66%

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

Alghawli

Taloba

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.

show abstract

Section: Related Workcontrasting

confidence: 66%