A NOVEL META-MACHINE LEARNING APPROACH TO DIAGNOSE STRESS FROM INDIVIDUAL FACTORS USING A SELF-RETRIEVED DATASET AND THEN PROVIDE DIRECTED TREATMENT
In: International Journal of Social Science and Economic Research, Band 6, Heft 12, S. 4933-4944
ISSN: 2455-8834
One of the main goals of machine learning is to make a General Artificial Intelligence. Currently, human artificial intelligence researchers work on meticulously manipulating model parameters by hand in order to arrive at highly optimized machine learning models. In the future, a system will be needed such that a software is able to completely arrive at an optimized model to a specific topic all by itself. An increasingly aware human problem is stress, which can oftentimes lead to a variety of health issues. Artificial intelligence (AI) algorithms, specifically Random Forests, have been employed to diagnose potential mental health illnesses due to a particular personal stress. Additionally, these algorithms would be manipulated by an automated hyperparameter manipulator, using extensive machine learning to find, sort, and train, validate, and test on a dataset all by itself