Uncertainty Resolution with Fuzzy Inference System Approach towards Stress, Anxiety, and Depression

Abstract

There is indisputable proof that stress, anxiety, and depression significantly and negatively impact people's well-being. Recently, problems with stress and sadness have frequently resulted in a variety of chronic health concerns or even mortality. It is important to remember that stress, anxiety, and depression are all dangerous and closely associated. According to a proverb, "Life is 10% what you experience and 90% how you respond to it." This suggests that how we react to and equally manage whatever happens to us depends on how we respond to it. Several unknowns make the condition more ambiguous, such as diverse symptoms and different underlying causes of health disorders. Fuzzy can benefit medical professionals, experts, hospitals, drugs, etc. by handling the ambiguity and uncertainty of such vast amounts of data on people in these circumstances. To solve so many ambiguities, gaps in knowledge, or imprecision, fuzzy logic is frequently used. The current experiment applies a fuzzy method with fuzzy logic in R to develop a fuzzy inference system for pattern identification and classification to increase performance. This focuses on creating a fuzzy rule foundation, model, and inference for the study of data related to stress, anxiety, and depression. The results show that using a fuzzy inference system for uncertainties aided in making decisions that could have resulted in more serious problems if not handled on time. This study should only be used to observe the symptoms and causes of stress, anxiety, and depression; it should not be used to treat the identified health problems. Hospitals are the best places to solve problems.