Prediction of Heart Disease Using Machine Learning Algorithms

Ravi, Sriram; Sambath, Dr.M.; Thangakumar, Dr.J.; Kumar, D. Ramana; NAVEEN, G V -; Bramiah, Makka

doi:10.47059/alinteri/v36i1/ajas21039

Cited by 2 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

Owusu-Adjei

Hayfron-Acquah

Twum

et al. 2023

Preprint

View full text Add to dashboard Cite

Focus on predictive algorithm and its performance evaluation is extensively covered in most research studies. Best predictive models offer Optimum prediction solutions in the form of prediction accuracy scores, precision, recall etc. Prediction accuracy score from performance evaluation have been used as a determining factor for appropriate model recommendations use. It is one of the most widely used metric for identifying optimal prediction solutions irrespective of context or nature of dataset, size and output class distributions between the minority and majority variables. The key research question however is the impact of using prediction accuracy as compared to balanced accuracy in the determination of model performance in healthcare and other real-world application systems. Answering this question requires an appraisal of current state of knowledge in both prediction accuracy and balanced accuracy use in real-world applications including a search for related works that highlight appropriate machine learning methodologies and techniques.

show abstract

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

Owusu-Adjei

Hayfron-Acquah

Twum

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Development of heart attack prediction model based on ensemble learning

Hasan

Saleh

2021

EEJET

View full text Add to dashboard Cite

With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem. Heart attack is one of the most common diseases and one of the leading causes of death, so finding a system that can accurately and reliably predict early diagnosis is an essential and influential step in treating such diseases. Researchers have used various data mining and machine learning techniques to analyze medical data, helping professionals predict heart disease. This paper presents various features related to heart disease, and the model is based on ensemble learning. The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. Furthermore, using machine learning algorithms (Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting) for prediction on the Framingham Heart Study dataset and compared with the proposed methodology. The results show that the feasibility and effectiveness of the proposed prediction method based on group learning provide accuracy for medical recommendations and better accuracy than the single traditional machine learning algorithm.

show abstract

Prediction of Heart Disease Using Machine Learning Algorithms

Cited by 2 publications

References 0 publications

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

Development of heart attack prediction model based on ensemble learning

Contact Info

Product

Resources

About