Abstract
In this study, a method is proposed in which accurate prediction at early stage is important of cardiac patient for efficiently treating. The novelty of this research is using feature extraction with the help of some techniques of signals processing. Ten different kinds of sensors are used are metal oxide semiconductors that are used for sensing the different gasses that are emanating from the human body. Moreover, ECG, SPO2 and oxygen sensors are used for further processing. Various experiments are performed that identify 5, 10, 15 and 20 subjects every subject is identified and scanned as 1000 different features. The signals that are received are analogue and with the help of Arduino they convert to digital signals. An architecture is trained on the dataset that is developed. Sensitivity, f-measures, accuracy and specificity are the standards that are used for the evaluation of the model that is proposed as identification of human odour. The accuracy for this model is more than 85%.
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Wazir, E., Gilanie, G., Rehman, N., Ullah, H., Mushtaq, M.F. (2022). Early Stage Detection of Cardiac Related Diseases by Using Artificial Neural Network. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_36
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