Abstract
Bipolar disorder is one of the most challenging illnesses where medical science is still struggling to achieve its landmark therapies. After reviewing existing prediction-based approaches towards investigating bipolar disorder, it is noted that existing approaches are more or less symptomatic and relates depression as sadness. It implies various theories that don't consider many precise indicators of confirming bipolar disorder. Therefore, this manuscript presents a novel framework capable of treating the dataset of depression and fine-tune it appropriately to subject it further to a machine learning-based predictive scheme. The proposed system subjects its dataset for a series of data cleaning operations followed by data preprocessing using a standard scale of rating bipolar level. Further usage of feature engineering and correlation analysis renders more contextual inference towards its statistical score. The proposed system also introduces a Recurrent Decision Tree that further contributes towards the predictive outcome of bipolar disorder. The outcome obtained showcases that the proposed scheme performs better than the conventional decision tree.
Highlights
The proposed paper is about performing prediction towards confirming if the subject is suffering from bipolar disorder on the basis of analysis of their mood-based statistics
bipolar disorder (BD) symptoms are associated with physical health problems, side effects, and social factors, and alcohol and drug abuse, which may contribute to BD symptoms in all individuals [6]
A more complex learning model can be seen in Huang et al [17], which is based on the joint approach of Convolutional Neural Network (CNN) and recurrent neural network (RNN) to identify short-term mood disorders
Summary
The proposed paper is about performing prediction towards confirming if the subject is suffering from bipolar disorder on the basis of analysis of their mood-based statistics. An important difference between unipolar depression and bipolar disorder is that episodes of mania characterize the latter This is characterized by inflated self-esteem, impulsive behavior, increased activity, decreased sleep, and goal-directed behavior [5]. The studies have shown that data quality has a significant impact on learning or predictive models This data type requires effective data modeling from the feature engineering viewpoint that exploits distinctive features [10]. Another problem is the selection of suitable data-driven approaches and learning models.
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