Left ventricular hypertrophy (LVH) is the heart condition where the walls of the left ventricle would be thicker than the normal condition. That obstructs the electrical activity of the heart and hence significantly decreases the pumping efficiency. LVH develops as the response to pressure overload that might arise from high blood pressure, stenosis etc. This condition of the heart can be reversed if diagnosed and treated in time. Its symptoms like short breathing, fatigue, palpitation of the heart, dizziness, chest pain after exercise are often misinterpreted or masked by the process of homeostasis, and it develops silently over the years. The prediction of presence of LVH using effective machine learning model would be helpful to have early diagnosis of the disease. This paper focuses on the prediction of Left Ventricular Hypertrophy (LVH) using the SVM machine learning model with the help of an electrocardiogram (ECG). The proposed methodology could be integrated with the IoT framework for smart healthcare solutions. Data is taken from the UCI machine learning repository i.e Cleveland, Hungary, Switzerland, and the VA Long Beach database consisting of 920 subjects. Data is split into 8:2 for training and testing purposes. Python 3.0 is used to analyze the data and the classification is verified by the 10-fold cross-validation technique. In this study, normal subjects and subjects with a high probability of LVH are classified successfully with greater ROC-AUC value than 0.5 which shows the unbiased classification. The results show that the proposed model with enhanced accuracy of 81+% and high specificity of 92.36% stands promising for futuristic smart healthcare applications.

No abstract