Accuracy enhancement with artificial neural networks for bipolar disorder prediction

Nisha Agnihotri, Sanjeev Kumar Prasad


The perfect physical health and mental wellbeing is an important aspect of human kind. Healthcare sectors involving machine learning and deep learning is providing good healthcare services is helping people for safeguarding them from being exploited with extra and unnecessary expenditures on medical check-ups. This gives treatments and many health services on time when needed. In this paper, different performance metrics are applied on online bipolar dataset named “Theory of mind in remitted bipolar disorder dataset” from Kaggle to evaluate the diagnosis for bipolar disorder feature prediction and analysis. In this study the proposed accuracy is better as compared to previous traditional models. As a result, artificial neural networks reduce the time taken in training and classification of dataset in prediction as given in result by optimal combination of epoch and hyperperameters.


Accuracy; Artificial neural networks; Deep learning; Machine learning; Performance metrics

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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