An Image Processing Approach for Detection of Prenatal Heart Disease

• Published in: BioMed research international Vol. 2022; pp. 2003184 - 14
• Main Authors: Selvan, Saravana, Thangaraj, S. John Justin, Samson Isaac, J., Benil, T., Muthulakshmi, K., Almoallim, Hesham S., Ali Alharbi, Sulaiman, Kumar, R. R., Thimothy, Sojan Palukaran
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; Hindawi Limited
• Subjects: Accuracy; Algorithms; Bayes Theorem; Bayesian analysis; Cardiovascular disease; Cardiovascular diseases; Congenital diseases; Coronary artery disease; Data mining; Data Mining - methods; Data processing; Datasets; Fatalities; Fetuses; Health risks; Heart; Heart diseases; Heart Diseases - diagnostic imaging; Humans; Illnesses; Image processing; Image Processing, Computer-Assisted; Machine learning; Medical diagnosis; Medical research; Patients; Pregnancy; Womens health
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2022/2003184

Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.