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Schizophrenia Prediction with the Adaboost Algorithm
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This paper presents an adaBoost approach for schizophrenia relapse prediction. The data for the adaBoost are extracted from patients answers to Early Warning Signs questionnaires sent regularly via mobile phone messages. The performance of the adaBoost algorithm is confronted with current ITAREPS system with sensitivity 0.65 and specificity 0.73. AdaBoost has the same sensitivity 0.65 but higher specificity 0.84 and is then ready to became the part of the ITAREPS care program.
Title: Schizophrenia Prediction with the Adaboost Algorithm
Description:
This paper presents an adaBoost approach for schizophrenia relapse prediction.
The data for the adaBoost are extracted from patients answers to Early Warning Signs questionnaires sent regularly via mobile phone messages.
The performance of the adaBoost algorithm is confronted with current ITAREPS system with sensitivity 0.
65 and specificity 0.
73.
AdaBoost has the same sensitivity 0.
65 but higher specificity 0.
84 and is then ready to became the part of the ITAREPS care program.
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