International Journal of Science, Engineering and TechnologyAuthors: Patanjal Singh, Arpan Chakraborty, Shivam Jana, Pranabesh Malik
Abstract: In today’s healthcare system, there is a clear shift from reactive treatment toward preventive and predictive care. Early detection of diseases and continuous monitoring of health conditions are becoming increasingly important for improving patient outcomes, reducing mortality rates, and lowering overall healthcare costs. With the rapid growth of digital medical records and advancements in artificial intelligence, machine learning has emerged as a powerful approach for analysing healthcare data and supporting medical decision-making. This dissertation, titled “Heart Health Prediction Using Machine Learning,” proposes an intelligent computational model designed to predict an individual’s overall health status and potential risk of diseases based on clinical and physiological parameters. The main goal of this work is to bridge the gap between traditional medical diagnosis and modern data-driven technologies by developing an automated system capable of efficiently analysing complex healthcare datasets. The proposed system utilizes a dataset containing important health indicators such as age, blood pressure, heart rate, glucose level, body mass index (BMI), cholesterol level, lifestyle factors, and other relevant medical attributes. To improve data quality and model performance, several preprocessing techniques were applied, including handling missing values, removing inconsistencies, feature selection, feature engineering, normalization, and dataset balancing. In addition, exploratory data analysis (EDA) was performed to identify patterns, correlations, and meaningful trends within the data. For prediction and classification, multiple supervised machine learning algorithms were implemented, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). These models were trained and evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. A comparative study was conducted to identify the most effective model for heart health prediction. Among all the tested algorithms, the Random Forest model showed the best performance due to its high accuracy, robustness, and ability to reduce overfitting through ensemble learning. The developed system acts as a decision-support tool that provides users with preliminary health assessments through a simple and user-friendly interface. By entering basic health information, users can receive insights into their current health condition and potential risk levels. This approach can be especially useful in remote or underserved areas where access to medical professionals is limited, thereby improving the accessibility of healthcare services. Furthermore, this study highlights the growing importance of artificial intelligence and machine learning in transforming healthcare systems. While the proposed model is not intended to replace professional medical diagnosis, it can assist healthcare providers by offering data-driven insights and supporting early decision-making processes. In conclusion, this dissertation demonstrates the successful application of machine learning techniques in healthcare for accurate health prediction and early disease detection. It also opens the door for future enhancements such as deep learning integration, real-time monitoring using IoT devices, and cloud-based deployment for large-scale healthcare solutions.
