Earticle

초록

영어

Heart illness is a serious disease faced by a significant populace worldwide. Considering death rates and large numbers of patients with cardiac illness, it is shown how important it is for a person to be subject for early diagnosis of the disease, as an average citizen cannot afford frequent expensive tests such as the ECG. So, an effective system needs to be put in place that is both practical and reliable for the prediction of the chances of heart disease. The extraction of medical data has become highly recommended so that the rate of death which is very high due to heart diseases can be predicted and treated. The development of a machine-based system for cardiac diagnosis provides a more accurate diagnosis than traditional methods. We therefore propose an app to prevent the vulnerability of heart disease given the fundamental symptoms, such as age, gender and rate of the pulse and so on. The neural networks of the machine learning algorithm have proven to be the most reliable and accurate algorithm used for this system.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. PROPOSED METHODOLOGY
A. Benchmark dataset
B. Preprocessing
C. Feature Selection
D. Tool
E. Classification
F. Training
G. Model testing
H. Implementation
IV. SIMULATION AND RESULTS
V. CONCLUSION AND FUTURE WORK
REFERENCES

저자

• Arif Wicaksono Septyanto [ School of Information System Duta Bangsa University Surakarta Indonesia ]
• Shanza Babar [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Syeda Binish Zahra [ Department of Computer Science Lahore Garrison University. Lahore, Pakistan ]
• Muhammad Adnan Khan [ Riphah School of Computing & Innovation, Faculty of Computing Riphah International University, Lahore Campus Lahore, Pakistan ]

참고문헌