Managing the Scarcity of Monitoring Data Through Machine Learning for Human Behavior in Mental Health Care

Nowadays, in many fields, including medicine and public health, developments in information and communication technology have introduced a revolution. Due to the absence of effective machinery algorithms and many possibilities to enhance patient understanding of chronic diseases, information gathered from the distinct sources were ignored. Therefore, researchers are investigating information shortages of computer training methods. This work recommends a fresh strategy to produce stronger predictive models specifically intended for the problems. In order to address the scarcity of data, works propose to make use of a semi-managed learning environment while using all unlabeled data sets. The techniques suggested were implemented in the mental health care field of bipolar disorder. The methods suggested enhancing the efficiency of ranking to the accuracy of ≈ 73% to ≈ 90%. The results have been achieved by overcoming previous methods that use traditional monitored methods of learning.