Reviewing the Effectiveness of Machine Learning Algorithm for Detecting Risk Behaviours Through Speech Patterns

Any risk behaviour may result in a negative outcome. This highly depends on the complex interplay of emotions and an individual's perception of risk. AI and ML can study the biological signature and speech of individuals, which can help clinicians intervene with individualised structured interventions.

This review investigates how AI and ML-based algorithms are used for detecting risk behaviours such as along with their diagnostic characteristics and treatment results. The review serves to collect all modern research about risk detection using existing ML techniques, along with their positive impact on clinical practice. The research explores how applying various DL models enhances the diagnostic accuracy and reliability of the findings.

Though many ML models show a strong potential in detecting the risk behaviours, they do face limitations like a sub-optimal level of precision and sensitivity, Limited clinical value, external validity, high false positive rates, and less interpretability. Hence, HMM is recommended as a good alternative because of its excellence in uncovering the hidden states from overt behaviours, especially using language or speech analysis. The research currently in the field of risk prediction works on text or speech analysis and uses neuroimaging data. The implementation of DL practice is needed through validation, and at the same time, ethical considerations, data privacy issues should also be considered. There is strong evidence to suggest that DL and ML models and their adaptations show promising ways to predict and prevent risky behaviours.