Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but it is far from being comfortable, rapid, and accessible to everyone.Speech analysis is an emerging non-invasive diagnostic tool, and a lot of researches have shown that it is efficient in speech recognition and in detecting Parkinson's disease, so can it be effective for differentiating between patients with cardiovascular disease and healthy people?This present work answers the question posed, by collecting a database of 75 people, 35 of whom suffering from cardiovascular diseases, and 40 are healthy. We took from each one three vocal recordings of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii… ..). By measuring dysphonia in speech, we were able to extract 26 features, with which we will train three types of classifiers: the k-near-neighbor, the support vectors machine classifier, and the naive Bayes classifier.The methods were tested for accuracy and stability, and we obtained 81% accuracy as the best result using the k-near-neighbor classifier.

Heart diseases cause many deaths around the world every year, and his death rate makes the leader of the killer diseases. But early diagnosis can be helpful to decrease those several deaths and save lives. To ensure good diagnose, people must pass a series of clinical examinations and analyses, which make the diagnostic operation expensive and not accessible for everyone.Speech analysis comes as a strong tool which can resolve the task and give back a new way to discriminate between healthy people and person with cardiovascular diseases. Our latest paper treated this task but using a dysphonia measurement to differentiate between people with cardiovascular disease and the healthy one, and we were able to reach 81.5% in prediction accuracy.This time we choose to change the method to increase the accuracy by extracting the voiceprint using 13 Mel-Frequency Cepstral Coefficients and the pitch, extracted from the people's voices provided from a database which contain 75 subjects (35 has cardiovascular diseases, 40 are healthy), three records of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii….) has been collected from each one. We used the k-near-neighbor classifier to train a model and to classify the test entities. We were able to outperform the previous results, reaching 95.55% of prediction accuracy.