COMPARATIVE ANALYSIS OD MACHINE LERNING ALGORITHMS FOR FAILURE PREDICTION IN COMPUTER HARDWARE MAITAINACE
Abstract
ABSTRACT
Predictive maintenance is crucial in minimizing downtime and optimizing computer hardware performance. This study compares the effectiveness of four machine learning algorithms - Decision Trees, Random Forest, Support Vector Machines (SVM), and AdaBoost in predicting hardware failures. Using a dataset of 1000 instances, the algorithms' performance was evaluated based on accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Results showed that Random Forest outperformed the other algorithms, achieving an accuracy of 90.5% and AUC-ROC of 0.95. The study concludes that machine learning algorithms can effectively predict computer hardware failures, with Random Forest being the most suitable algorithm for this task.
KEYWORDS: Predictive maintenance; Machine learning; Hardware failure prediction; Random Forest; Classification algorithms; AUC-ROC.
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