Volume 2 Issue 2 | 2025 | View PDF
Paper Id:IJMSM-V2I2P104
doi: 10.71141/30485037/V2I2P104

Predicting Heart Disease with Machine Learning: Enhancing Accuracy through Algorithmic Approach

Vitthal B Kamble, Bhoomi Gulabani, Srushti Narkhede, Shital Godse

Citation:
Vitthal B Kamble, Bhoomi Gulabani, Srushti Narkhede, Shital Godse, "Predicting Heart Disease with Machine Learning: Enhancing Accuracy through Algorithmic Approach" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 2, pp. 36-56, 2025.

Abstract:
Cardiovascular disease, or CVD, kills millions of people each year worldwide due to a variety of intricate and interrelated causal factors. Because it is the leading cause of death in the world, the detection and diagnosis of heart disease at an early stage is regarded as being of the utmost significance in public health. Over the last few years, advances in machine learning technology have been an invaluable resource in the healthcare sector, allowing precise predictions of disease when such programs are well-trained and rigorously tested. In cardiac-related diseases, early detection is most crucial to allow patients to receive the best treatment possible before it becomes a critical and potentially fatal condition. The accuracy of such high-accuracy predictions is not only worth its weight in gold for enhanced patient outcomes but also for saving lives ultimately. This study was thus conducted with the aim of creating a successful model for heart disease prediction specifically using the Random Forest algorithm. With the use of the random forest algorithm in our study research, we achieved a remarkable accuracy rate of 100%.

Keywords: Artificial Intelligence, Data Analysis, Heart Disease, Machine Learning, Prediction Model, Random Forest Algorithm.

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