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Predicting Coronary Heart Disease through Machine Learning Algorithms

EasyChair Preprint no. 11904, version 2

Versions: 12history
8 pagesDate: February 13, 2024


Machine learning has gained popularity in medical fields due to the increasing availability of health data and the improvement of machine learning algorithms. It can be used to create predictive models that diagnose diseases, predict disease progression, tailor treatment to individual patient needs, and improve the func-tioning of medical systems. The right use of data can have a positive impact on improving the quality of patient care, reducing healthcare costs, and creating tai-lored and effective medical approaches. The healthcare sector benefits greatly from the accurate interpretation of medical data as it contributes to the early pre-diction of diseases in patients. Early detection of a disease can help control the symptoms and provide the correct treatment. In our work, we analyzed actual measurements from the Framingham Heart Study and we created a medical data-base with 78001 records. Our ultimate goal is to develop an expert Artificial In-telligence system and an Artificial Neural Network that can predict the develop-ment of coronary heart disease by employing intelligent knowledge-mining algo-rithms. We have created two intelligent systems that predict the progression of coronary heart disease using machine learning algorithms such as Random For-est, Decision Trees and Neural Networks. In our experimental analysis, the Deci-sion Tree and Neural Network achieved an accuracy of 90.08% and 84.56% re-spectively.

Keyphrases: Big Data, Coronary Heart Disease, intelligent algorithms, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Savina Mariettou and Constantinos Koutsojannis and Vassilios Triantafillou},
  title = {Predicting Coronary Heart Disease through Machine Learning Algorithms},
  howpublished = {EasyChair Preprint no. 11904},

  year = {EasyChair, 2024}}
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