Classification Performance Between Machine Learning and Traditional Programming in Java

Detta är en Kandidat-uppsats från Högskolan Kristianstad/Fakulteten för naturvetenskap; Högskolan Kristianstad/Fakulteten för naturvetenskap

Sammanfattning: This study proposes a performance comparison between two Java applications with two different programming approaches, machine learning, and traditional programming. A case where both machine learning and traditional programming can be applied is a classification problem with numeric values. The data is heart disease dataset since heart disease is the leading cause of death in the USA. Performance analysis of both applications is carried to state the differences in four main points; the development time for each application, code complexity, and time complexity of the implemented algorithms, the classification accuracy results, and the resource consumption of each application. The machine learning Java application is built with the help of WEKA library and using its NaiveBayes class to build the model and evaluate its accuracy. While the traditional programming Java application is built with the help of a cardiologist as an expert in the field of the problem to identify the injury indications values. The findings of this study are that the traditional programming application scored better performance results in development time, code complexity, and resource consumption. It scored a classification accuracy of 80.2% while the Naive Bayes algorithms in the machine learning application scored an accuracy of 85.51% but on the expense of high resource consumption and execution time.

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