Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance.

Abstract

Abstract-Software process modelling has recently become an area of interest within both academia and industry. It aims at defining and formalizing the software process in the form of formal rigorous models. A software process modelling formalism presents the language or notation in which the software process is defined and formalized. Several software process modelling formalisms have been introduced lately, however, they have failed to gain the attention of the industry. One major objective of formalizing the software process that has ever been an issue of research, is to enhance the understanding and communication among software process users. To achieve this aim, a modelling formalism has to offer a common language to be wellunderstood by all software process users. BPMN presents a graphical-based widely accepted standard formalism, mainly aimed at business process modelling. This paper illustrates a software process modelling formalism based upon BPMN specifications for representing the software process, named as, SP2MN. The paper also demonstrates the applicability and evaluation of the proposed formalism by; utilizing the standard ISPW-6 benchmark problem, in addition to comparing the expressiveness of SP2MN with similar software process modelling formalisms. The evaluations prove that SP2MN contributes in enhancing software process formalization. SP2MN, accordingly, can be used as a standard software process modelling formalism.