Heart Disease Prediction using Machine Learning Techniques

By Devansh Shah, Samir B. Patel, S. Bharti. SN Computer Science, 2020

789 citations recorded by the source corpus.

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/e6b733b960cc1800487203af61de1c58b6299d5a

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Citations in the corpus

Effective Heart Disease Prediction Using Machine Learning Techniques (2023, background): “In a study conducted by Shah et al. (2020) [18], the authors aimed to develop a model for predicting cardiovascular disease using machine learning techniques.”
Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, background): “Shah et al. [40] proposed a system to study different conditions that can affect the heart and primary factors for the deaths.”
Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review (2021, methodology): “Among all algorithms, SVM demonstrated the best performance by achieving an accuracy of 95% [43].”