Predicting the Risk of Cardiovascular Diseases using Machine Learning Techniques

Authors

  • Puneet Garg Assistant Professor, ABES Engineering College, Ghaziabad, U.P., India
  • Neetu Sharma Professor, Galgotias University, Greater Noida, U.P., India
  • Sonal Associate Professor, Department of CSE, BPSMV, Khanpur kalan, Distt. Sonipat, Haryana, India
  • Bharati Shukla Assistant Professor, ABES Engineering College, Ghaziabad, U.P., India

Keywords:

Coronary ailment, Heart disease, ECG, Data mining, Respiratory disappointments

Abstract

These days, health-related diseases are increasing day by day due to lifestyle and genetics. Especially these days, heart disease is so common that people's lives are at risk. Blood pressure, cholesterol and pulse rate vary from person to person. However, according to proven clinical results, normal blood pressure is 90/120 and cholesterol is 129-100 mg/dL, Pulse 72, fasting blood glucose 100 mg/dL, heart rate 100-60 bpm, normal ECG, main vessel width 25 mm (1 inch) in the aorta only 8 μm in the capillaries. This article looks at the different classification techniques used to predict each person's risk level based on age and gender. Blood pressure, cholesterol, heart rate. A "disease prediction" system based on predictive modeling predicts a user's disease based on the symptoms the user enters into the system. The system analyzes the symptoms that the user provides as inputs and provides disease probabilities as outputs. Disease prediction is done by applying techniques like KNN, Decision tree classifiers, random forest algorithms, and more. This technique calculates the probability of a disease. Therefore, we obtain an average prediction accuracy probability of 86.48%.

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Puneet Garg ✌

Smoking effects on heart

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Published

31.01.2023

How to Cite

Garg, P. ., Sharma, N. ., Sonal, & Shukla, B. . (2023). Predicting the Risk of Cardiovascular Diseases using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 165 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2520

Issue

Vol. 11 No. 2s (2023)

Section

Research Article

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Copyright (c) 2023 Puneet Garg, Neetu Sharma, Sonal, Bharati Shukla

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