Mental Health Illness

The necessity for efficient medical care and the rise of mental health issues have resulted in an investigation of machine learning that can be applied in mental health problems. This paper presents a recent comprehensive analysis of machine learning techniques for mental health issue prediction.         We have collected research articles and studies that are related to the machine learning approaches in  predicting mental health problems by searching reliable databases. We include a total of 06 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as bipolar disorder, sadness and anxiety, and children’s mental health issues. In the era of an ageing population, young adults on medical wards are quite rare, as only 12% of young adults report a long-term illness or disability. However, mental health problems remain prevalent in the younger population. In a recent report, mental health and obesity were listed as the most common problems in young adults. Teams set up specifically for the needs of younger adults, such as early intervention in psychosis services are shown to work better than traditional care and have also proven to be cost effective. On the medical wards, younger patients may elicit strong emotions in staff, who often feel protective and may identify strongly with the young patient’s suffering. In order to provide holistic care for young adults, general physicians need to recognise common presentations of mental illness in young adults such as depression, deliberate self-harm, eating disorders and substance misuse. Apart from treating illness, health promotion is particularly important for young adults.