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An Image Processing Approach for Detection of Prenatal Heart Disease
Saravana Selvan1, S John Justin Thangaraj2, J Samson Isaac3
1Faculty of Engineering & Computer Technology, AIMST University, Bedong, Kedah 08100, Malaysia.
Insights
This study predicts cardiovascular disease (CVD) risk using data mining. The Naïve Bayes classifier demonstrated the highest accuracy in identifying individuals likely to develop heart problems.
Area of Science:
- Medical Informatics
- Cardiology
- Data Science
Background:
- Congenital heart defects (CHDs) are a leading cause of global mortality.
- Accurate early detection of cardiovascular disease (CVD) risks is crucial.
- Data preprocessing and mining are vital for analyzing complex medical data.
Purpose of the Study:
- To develop a predictive model for cardiovascular disease.
- To evaluate the effectiveness of various data mining classification algorithms for CVD risk prediction.
- To identify key attributes for accurate CVD forecasting.
Main Methods:
- Utilized a dataset of 300 patients with heart disease from a medical research database.
- Selected 15 relevant attributes from an initial set of 75 for analysis.
- Employed classification models including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF).
Main Results:
- The Naïve Bayes classifier achieved the highest overall accuracy in predicting cardiovascular disease.
- Analysis focused on 15 key variables to establish the utility of different predictive approaches.
- The study demonstrates the potential of data mining for early CVD risk identification.
Conclusions:
- Naïve Bayes is a highly effective algorithm for predicting cardiovascular disease risk.
- Data mining techniques offer a promising avenue for improving early detection and management of heart conditions.
- Further research can refine these models for clinical application in preventing CVD.
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.
