Comprehensive Behavioral Health Analysis Using Adaptive Neuro-Fuzzy Inference Systems and Soft Computing with Digital Data

Digital data analysis in mental health, adaptive neurofuzzy inference systems, and soft computing. It also stresses that conventional approaches to assessing mental health and different patterns of behavior are often inaccurate since they rely on self-reports. This chapter presents a novel method for improved behavioural health pattern recognition and modelling using ANFIS. The method combines neural networks with fuzzy logic. Behavioral health indices, which include physical activity, sleep, and mood, may be monitored via the use of digital data retrieved from personal gadgets, such as fitness trackers and smartphones. When working with behavioral data, which is inherently unpredictable and ambiguous, soft computing approaches are also discussed in the context of implementation. The ANFIS system, enhanced with machine learning algorithms, is presented as a dependable method for behavioral health intervention prediction modelling, early warning sign risk assessment, and personalized feedback. At the end of the chapter, the main points are reviewed again: the potential of soft-computing, and especially neuro-fuzzy systems, to improve the detection and measurement of psychological and behavioral health; these systems provide more adaptable and precise models than the traditional approaches. The efficiency of the following treatment processes and the reliability of the behavioral evaluation are two anticipated benefits of using such systems.