Key result
Novel machine learning model leverages phonocardiograms and clinical data to improve early heart disease diagnosis.
Machine learning models utilizing patient data and heartbeat sounds are proposed to improve early diagnosis of heart disease.
ML-based heart disease prediction may enable earlier detection; hypothesis-generating and requires prospective validation before clinical adoption.
There are many deadly diseases present in the huge population of the people in the world, Heart diseases are some of them. When death rates are considered it is known that many p e o p l e a r e su f f e r i ng from hear diseases since these diseases are very dangerous early diagnosis is very important. The major cause of death is Heart diseases according to WHO (World Health Organization). Using a standard method for diagnosis is not good enough fo these diseases. We need to develop a good medical diagnosis model which uses machine learning algorithms and techniques for prediction of diseases an h i s g i v e s an accurate diagnosis and results than the standard method. By predicting the disease in an earlier stage it reduces the cost of treatment and it also plays an essential role in the treatment. This prediction system’s application is to take the input of the patient’s data and heartbeat sound recordings and predict the diseases.
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Bhaskar et al. (2020) studied Heart disease. Machine learning algorithms vs. Standard method for diagnosis was evaluated on Prediction of heart diseases. A machine learning prediction system using patient data and heartbeat sound recordings is proposed to improve the early diagnosis of heart diseases compared to standard methods.
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