INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 106
Heart Disease Prediction Using Machine Learning Algorithms
Ansh soni, Prof. Rajendra Arakh, Prof. Priyanka Jain, Anshul Singh
Student, Computer Science Engineer, Shri Ram Institute of Technology
DOI: https://doi.org/10.51583/IJLTEMAS.2025.140500015
Received: 17 May 2025; Accepted: 21 May 2025; Published: 31 May 2025
Abstract: This study develops a machine learning model for predicting heart disease risk using patient data, including
demographics, medical history, and clinical measurements. Various algorithms such as Decision Trees, Support Vector Machines
(SVM), and Neural Networks are evaluated for their predictive accuracy. The aim is to assist clinicians in early diagnosis and
intervention. The model is evaluated using accuracy, precision, recall, and F1-score, and focuses on building a robust tool for heart
disease prevention
I. Introduction
Heart disease is a leading cause of death worldwide. Early detection is critical for prevention and improving survival rates.
Traditional diagnostic methods are often subjective and time-consuming. This paper presents a machine learning approach to heart
disease prediction based on patient data. The system leverages demographic and clinical features to classify the likelihood of heart
disease.
Problem Statement
Current diagnostic methods for heart disease can be slow, inconsistent, and require expensive medical tests. This research aims to
develop a system that quickly and accurately predicts heart disease using machine learning techniques and patient data. Such a
model would support timely medical decisions and interventions.
Objective
The primary objective is to build a predictive model that can classify whether a patient is at risk of heart disease using clinical
attributes such as:
Age
Sex
Resting blood pressure
Cholesterol level
Chest pain type
Fasting blood sugar
ECG result
This will help in early diagnosis and reduce mortality rates
Technology Used
Machine Learning Algorithms: -
Decision Trees: For classification tasks using input features.
Support Vector Machines (SVM): For decision boundary optimization.
Neural Networks: For capturing complex feature interactions.
Data Preprocessing Tools: -
Pandas: For data handling and preprocessing.
NumPy: For numerical operations and arrays
Model Evaluation: -
Scikit-learn: Used for training, testing, and performance evaluation (accuracy, precision, recall, F1-score).
Visualization: -
Matplotlib & Seaborn: Used for visualizing datasets and model metrics.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 107
Dataset Source: -
UCI Heart Disease Dataset: Used for model training and validation.
Proposed Solution
We propose a heart disease prediction model powered by machine learning. It processes structured patient data, uses preprocessing
techniques such as label encoding and normalization, and trains multiple classifiers to find the best-performing model. A user-
friendly Gradio interface allows healthcare providers to input patient data and receive predictions in real time.
System Flow Diagram
Fig: -1
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 108
II. Result
Fig: -2
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
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Proposed Solution Flow Diagram
Fig: -3
III. Conclusion
A machine learning-based system for heart disease prediction shows significant potential for assisting clinical diagnosis. With
further refinement and deployment, such systems can offer low-cost, high-speed diagnostic support
IV. Acknowledgements
I would like to express my deep sense of gratitude and sincere thanks to my guide Prof. Rajendra Arakh and Prof. Priyanka
Jain, Department of Computer Science & Engineering at Shri Ram Institute of Technology, Jabalpur for his valuable and ever
willing precious guidance , technical support and constant encouragement during the course of this project work. It was pleasure
and unique experience for me to work under their guidance.
I am grateful to Prof. Brajesh Patel, Head of Department of Computer Science & Engineering and other staff members of Computer
Science and Engineering Department for providing the necessary facilities for the successful completion of this work.
Finally, my greatest thanks to my family for their patience and understanding.
References
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User Input Form(Gradio
Frontend)
Data Preprocessing
- Handle categorical
variables (one-hot)
- Scale numerical data
Trained ML Model (e.g.,
Logistic Regression or
Random Forest)
Prediction Output
- Heart Disease Detected
- No Heart Disease
Detected
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 110
7. Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2015). Classification of heart disease using k-nearest neighbor and genetic
algorithm. Procedia Computer Science, 85, 862–870.
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