Exploring Heart Disease Diagnosis using Multivariate Data Analysis: A Comparative Study of Naive Bayes and Logistic Regression
Abstract
This research paper delves into the intricate task of heart disease diagnosis, utilizing a multivariate dataset encompassing 14 distinct attributes. The dataset, commonly referred to as the Cleveland database, has been widely adopted in machine learning research. With 606 instances and attributes spanning age, sex, medical parameters, and electrocardiographic results, the primary objectives of this study are two-fold: Firstly, to develop predictive models that accurately identify the presence of heart disease based on patient attributes, and secondly, to unearth valuable insights from the dataset that contribute to a deeper understanding of this critical health concern. Two classification algorithms, Naive Bayes and Logistic Regression, are employed and their performance in terms of accuracy, precision, and recall are compared.
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Introduction
Step by step the instances of heart infections are expanding at a quick rate and it's vital and worried to foresee any such sicknesses ahead of time. This conclusion is a troublesome errand for example it ought to be performed exactly and productively. Cardiovascular sicknesses are extremely normal nowadays, they depict a scope of conditions that could influence your heart. World wellbeing association assesses that 17.9 million worldwide passings from (Cardiovascular illnesses) CVDs [1][2]. It is the essential explanation of passings in grown-ups. Our undertaking can assist with foreseeing individuals who are probably going to determine to have a coronary illness by help of their clinical history [5][6]. It perceives who all are having any side effects of coronary illness, for example, chest torment or hypertension and can assist in diagnosing sickness with less clinical trials and successful therapies, so they can be restored appropriately [12]. The exploration paper fundamentally centers around which patient is bound to have a coronary illness in light of different clinical qualities. We arranged a coronary illness forecast framework to foresee whether the patient is probably going to be determined to have a coronary illness or not utilizing the clinical history of the patient.
Conclusion
This study showcases the potential of Naive Bayes and Logistic Regression in heart disease diagnosis using multivariate data analysis. The high performance metrics and the insights gained from the dataset underscore the significance of machine learning in healthcare and motivate further research to improve diagnostic accuracy and patient outcomes.
Future work could explore the incorporation of additional advanced machine learning techniques and feature engineering to enhance diagnostic accuracy further. Moreover, extending this research to diverse datasets and incorporating more attributes may improve the robustness and generalizability of the models.