Techniques for Detecting Human Psychological Disorders Using Machine Learning and Soft Computing
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內容簡介
Techniques for Detecting Human Psychological Disorders Using Machine Learning and Soft Computing refers to the application of computational methods to identify and diagnose various mental health conditions in humans. This involves the use of machine learning and soft computing algorithms to analyze data and patterns related to psychological disorders, such as neuroimaging, speech analysis, and behavioral data. The objective of these techniques is to develop diagnostic tools and predictive models that can aid mental health professionals in the accurate and efficient diagnosis of psychological disorders. By using computational models to analyze data, these techniques have the potential to identify patterns that are not easily detectable by humans, leading to earlier and more accurate diagnoses, and ultimately improving the treatment outcomes for individuals with psychological disorders.
The use of machine learning and soft computing in detecting human psychological disorders is a rapidly growing field of research with potential applications in clinical and non-clinical settings. By leveraging large amounts of data and complex algorithms, these techniques can assist in the early detection of mental health conditions, providing an opportunity for early intervention and prevention of more severe symptoms. These methods can also help mental health professionals to develop personalized treatment plans for patients based on their individual profiles, leading to more effective and efficient care. The use of machine learning and soft computing in this area can also help to reduce the stigma associated with mental health disorders by providing objective and data-driven diagnosis and treatment recommendations. However, the application of these techniques also raises ethical considerations, including issues related to privacy, bias, and the potential for misinterpretation of results. Despite these challenges, the continued development of machine learning and soft computing techniques for detecting psychological disorders holds great promise for improving mental health outcomes and quality of life for individuals with these conditions.
Psychological disorders are chronic and critical human disorders that may affect the
overall personality, behaviour and health of a person . A psychological disorder is a condition
characterized by abnormal thoughts, behaviours and feelings that can have significant impact on
person's everyday functioning and existence. As per Diagnostic and Statistical Manual of Mental
Disorders (DSM-5), psychological disorders are classified into twenty main categories
viz. neurodevelopment disorder, schizophrenia, psychotic disorders, depression, anxiety, trauma-
and stressor-related disorders, obsessive-compulsive and related disorders, dissociative
disorders, feeding and eating disorders, elimination disorders, sleep-wake disorders,
sexual dysfunctions, gender dysphoria, disruptive, impulsive and conduct disorders,
substance-related and the addictive disorders, neurocognitive disorders, personality
disorders, paraphilic disorders and other mental disorders
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