Bipolar Diseased Student Performance Prediction using Machine Learning-Multi Tier Tier Performance Analysis Approach
Keywords:
Machine learning, bipolar disorder, Confusion matrix, Long Short-Term Memory (LSTM)Abstract
Severe mood swings and extreme highs and lows characterize a mental health condition termed bipolar disorder. This is probably the most common abnormality in connection with mental health, and people of every age fail to recognize it. In general, bipolar disorder occurs in families, though not all siblings will be impacted by it or have the same genetics and risks factors. Here, we combine the information gathered from Magnetic Resonance Imaging (MRI) with the random forests method. These discrepancies are useful in differentiating particular bipolar disorder patients from individuals with psychological issues that are under medical control. A closer look into majority of the existing schemes using machine learning shows dataset is directly applied to machine learning algorithm without much emphasis towards working on features. The complications of determining an exact state of bipolar disorder is quite challenging and demands deeper insights towards understand the trend of behavior. Unfortunately, there are few reported studies in existing scheme where machine learning has been introduced with more emphasis towards feature management process. Majority of the existing machine learning-based framework towards mental illness are not assessed over benchmarked test environment. Due to this absence, the claims of accuracy reported in existing system cannot be justified to be working on real-time environments too. Hence, lack of benchmarking doesn’t only reduce the applicability but also acts as an impediment towards further optimization.
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