9786630094183 - Disease Prediction Using Machine Learning: Medical Analysis Using AI de Bhuvaneswari, A P (4 resultados)

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -'Machine Learning Models to Optimize the Performance of the Classifier for Early Prediction of the Disease'The core engineering and computing methodology addresses critical gaps in medical diagnostic models through a distinct multi-stage workflow:High-dimensional clinical data (such as complex patient records) often introduces computational noise and 'the curse of dimensionality.' The research utilizes specialized engineering methodologies-combining techniques like Principal Component Analysis (PCA) and tailored feature selection-to streamline datasets down to their most statistically significant risk factors.Before classification occurs, the framework integrates unsupervised machine learning techniques, such as optimized K-means clustering, to discover hidden patterns and naturally segment clinical data structures.The core contribution of the thesis centers on engineering specific models to optimize the accuracy, sensitivity, and execution performance of standard supervised ML classifiers. By fine-tuning these underlying mathematical classifiers, the system minimizes false positives and false negatives. 128 pp. Englisch.

Taschenbuch. Condición: Neu. Disease Prediction Using Machine Learning | Medical Analysis Using AI | A P Bhuvaneswari | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630094183 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -'Machine Learning Models to Optimize the Performance of the Classifier for Early Prediction of the Disease'The core engineering and computing methodology addresses critical gaps in medical diagnostic models through a distinct multi-stage workflow:High-dimensional clinical data (such as complex patient records) often introduces computational noise and 'the curse of dimensionality.' The research utilizes specialized engineering methodologies-combining techniques like Principal Component Analysis (PCA) and tailored feature selection-to streamline datasets down to their most statistically significant risk factors.Before classification occurs, the framework integrates unsupervised machine learning techniques, such as optimized K-means clustering, to discover hidden patterns and naturally segment clinical data structures.The core contribution of the thesis centers on engineering specific models to optimize the accuracy, sensitivity, and execution performance of standard supervised ML classifiers. By fine-tuning these underlying mathematical classifiers, the system minimizes false positives and false negatives. 128 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Machine Learning Models to Optimize the Performance of the Classifier for Early Prediction of the Disease'The core engineering and computing methodology addresses critical gaps in medical diagnostic models through a distinct multi-stage workflow:High-dimensional clinical data (such as complex patient records) often introduces computational noise and 'the curse of dimensionality.' The research utilizes specialized engineering methodologies-combining techniques like Principal Component Analysis (PCA) and tailored feature selection-to streamline datasets down to their most statistically significant risk factors.Before classification occurs, the framework integrates unsupervised machine learning techniques, such as optimized K-means clustering, to discover hidden patterns and naturally segment clinical data structures.The core contribution of the thesis centers on engineering specific models to optimize the accuracy, sensitivity, and execution performance of standard supervised ML classifiers. By fine-tuning these underlying mathematical classifiers, the system minimizes false positives and false negatives.