article · Scientific African
The Covid-19 pandemic caused significant harm to global mental health, increasing rates of depression which can lead to severe physical ailments and suicide. Early detection is vital to prevent these outcomes. To address this need, research analysed responses to a twenty-one question survey based on the Hamilton tool and psychiatric guidance. Three machine learning techniques, namely Decision Tree, K-Nearest Neighbours, and Naive Bayes, were implemented using Python to analyse the survey data and compare their diagnostic performance. K-Nearest Neighbours achieved superior accuracy in detecting depression, whilst the Decision Tree algorithm demonstrated better performance regarding latency. Based on these findings, an alternative machine learning approach is proposed to replace traditional detection methods by asking encouraging questions and collecting regular participant feedback.
Depression affects millions globally and carries risks of secondary physical diseases and suicide, impacts intensified by the Covid-19 crisis. Traditional screening methods can be slow and resource-intensive. Applying machine learning models to standard clinical questionnaires provides faster, automated early detection, enabling timely clinical intervention and potentially preventing severe illness or premature death.
This research points toward an early-stage screening tool that could assist healthcare providers and digital health platforms. By applying automated classification to survey responses, the approach could be integrated into telehealth services or mental wellness applications. As the study is an early algorithmic comparison, extensive software development and clinical testing would be needed before any commercial adoption.
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Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression. Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them.
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DOI: 10.1016/j.sciaf.2023.e01716
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