[Retracted] An Image Processing Approach for Detection of Prenatal Heart Disease

Abstract

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.

Improved Smart Healthcare System of Cloud-Based IoT Framework for the Prediction of Heart Disease

“…Autoencoder is a network. The inp designed in au autoencoders has [23]. Therefore, the c features is trai Subject to constraints:…”

“…The input selected featured vectors are designed in autoencoder. The format of autoencoders has an encoder-decoder structure [23].…”

Retracted: An Image Processing Approach for Detection of Prenatal Heart Disease

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”