9789999335102 - Mental Health Analysis based on Machine Learning von Mangal, Priyanka; Patidar, Harish (10 Ergebnisse)

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Paperback. Zustand: new. Paperback. Mental health illness prediction system using machine learning algorithms focuses on the use of artificial intelligence techniques to support early detection of mental health conditions. Here, students are considered as target population. This book explores how machine learning algorithms can be effectively applied to predictive models of mental health illnesses to predict any kind of disorder among students so that early precaution can be taken. It presents a structured framework for designing and implementing an intelligent prediction system based on machine learning, covering data preparation includes data collection, data preprocessing, feature extraction, algorithm selection techniques for model development, training, testing, and system deployment, performance analysis. In addition to theoretical insights, the book presents real-world case studies that illustrate the deployment of predictive models in mental health applications. It highlights various machine learning algorithms and their role in improving prediction accuracy and decision support. The content addresses real-world challenges for students, developers, and healthcare researchers and professional audiences, the book bridges the gap between mental health studies and intelligent computing technologies. It supports early diagnosis and decision-making in mental health care. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. Mental health illness prediction system using machine learning algorithms focuses on the use of artificial intelligence techniques to support early detection of mental health conditions. Here, students are considered as target population. This book explores how machine learning algorithms can be effectively applied to predictive models of mental health illnesses to predict any kind of disorder among students so that early precaution can be taken. It presents a structured framework for designing and implementing an intelligent prediction system based on machine learning, covering data preparation includes data collection, data preprocessing, feature extraction, algorithm selection techniques for model development, training, testing, and system deployment, performance analysis. In addition to theoretical insights, the book presents real-world case studies that illustrate the deployment of predictive models in mental health applications. It highlights various machine learning algorithms and their role in improving prediction accuracy and decision support. The content addresses real-world challenges for students, developers, and healthcare researchers and professional audiences, the book bridges the gap between mental health studies and intelligent computing technologies. It supports early diagnosis and decision-making in mental health care. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Zustand: Neu. Mental Health Analysis based on Machine Learning | Priyanka Mangal | Taschenbuch | Englisch | 2026 | Eliva Press | EAN 9789999335102 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mental health illness prediction system using machine learning algorithms focuses on the use of artificial intelligence techniques to support early detection of mental health conditions. Here, students are considered as target population. This book explores how machine learning algorithms can be effectively applied to predictive models of mental health illnesses to predict any kind of disorder among students so that early precaution can be taken. It presents a structured framework for designing and implementing an intelligent prediction system based on machine learning, covering data preparation includes data collection, data preprocessing, feature extraction, algorithm selection techniques for model development, training, testing, and system deployment, performance analysis.In addition to theoretical insights, the book presents real-world case studies that illustrate the deployment of predictive models in mental health applications. It highlights various machine learning algorithms and their role in improving prediction accuracy and decision support. The content addresses real-world challenges for students, developers, and healthcare researchers and professional audiences, the book bridges the gap between mental health studies and intelligent computing technologies. It supports early diagnosis and decision-making in mental health care.