Objective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman’s feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.
According to the data of 2020, it is seen that 1 of every eight cancers diagnosed worldwide and the 5th among cancers that cause death is breast cancer. Cancer can spread to different organs and reach an incurable stage in patients who are not diagnosed and treated at the right time. Therefore, reducing the time taken for breast cancer diagnosis and reducing mortality rates are of great importance for accurate and early diagnosis of the disease. This study aims to improve the accuracy of cancer detection by using various machine learning algorithms and methods for artificial intelligence-based breast cancer diagnosis. By using ultrasonography images taken from 780 people, image information processed with statistical parameters was extracted. Artificial intelligence-based breast cancer detection was performed by applying three different machine learning algorithms and the hybrid machine learning algorithm designed as a combination of these algorithms on the extracted data set. In this way, early detection of cancerous cells will be carried out without creating advanced risks for the individual, and treatment will be possible.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.