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Price distribution over 42 days • 1 price levels

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Most common price: £58 (42 days, 100.0%)

Price range: £58 - £58

Price levels: 1 different prices over 42 days

Description

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.

Product Specifications

Format: paperback
ASIN: 9999335101
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 06 January 2026
Listed Since: 10 January 2026

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Mental Health Analysis based on Machine Learning

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It has never been this cheap. We have no record of a lower price.

Mental Health Analysis based on Machine Learning

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

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