Vorhersage von Brustkrebs mittels Data Mining
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Bachelorarbeit aus dem Jahr 2022 im Fachbereich Ingenieurwissenschaften - Computertechnik, Note: 1, Fachhochschule St. Pölten (Data Science und Business Analytics), Sprache: Deutsch, Abstract: Eine Möglichkeit um präzise Vorhersagen von Erkrankungen zu treffen und medizinische Daten besser verstehen zu können, ist die Verwendung von Data Mining im Gesundheitsbereich. Data Mining ist eine Technik, um Informationen aus gro en Mengen Daten zu extrahieren. Die Auswertung medizinischer Daten mit Hilfe von Machine Learning und Künstlicher Intelligenz kann Ärzt/inn/en dabei unterstützen, bessere Entscheidungen zu treffen, Diagnosen zu stellen und die Arbeitslast insgesamt zu minimieren. Data Mining bietet viele Methoden, um Informationen aus medizinischen Datensets zu erhalten. Techniken wie Klassifikation, Clustering und Rule Mining können für die Analyse von Daten verwendet werden. Die Vorhersage von Erkrankungen ist nur ein Beispiel für den Einsatz von Data Mining im Gesundheitsbereich. Durch die Nutzung von maschinellem Lernen könnte die Anzahl an Tests und die Zeit, die benötigt werden, um eine Krankheit zu diagnostizieren, verringert werden. Einige Paper und Artikel haben sich bereits mit verschiedenen Data Mining Methoden für die Vorhersage von Erkrankungen beschäftigt und diese miteinander verglichen. Kumari et al. verglichen beispielsweise KNN, Logistische Regression und Support Vector Machine miteinander, wobei der KNN die höchste Accuracy erreichte. Diese Arbeit stellt sich nun ebenfalls die Frage, ob die Genauigkeit bei unterschiedlichen Methoden voneinander abweicht oder sehr ähnlich ist. In dieser Bachelorthesis soll geklärt werden, welche der drei Klassifikatoren, KNN, SVM und Entscheidungsbaum, für die Vorhersage von Brustkrebs am besten geeignet ist. Dabei wird die Genauigkeit (Accuracy) der unterschiedlichen Methoden miteinander verglichen und eine Entscheidung über die Eignung getroffen. Au erdem wird getestet, ob eine anfängliche Feature Selection (A
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