Combatting Heart Diseases: Advanced Predictions Using Optimized DNN Architecture

Mochammad Abdul Azis, Sumarna Sumarna

Submitted : 2023-10-26, Published : 2023-11-30.

Abstract

Heart disease has become a global health issue and is recorded as one of the primary causes of death in many countries. In this modern era, with rapid technological advancements and shifting lifestyles, numerous factors contribute to the increasing prevalence of heart diseases. These range from dietary habits, lack of physical activity, stress, to genetic factors. Given the complexity of this ailment, information technology plays a crucial role in providing innovative solutions. One of them is predicting the risk of heart disease, enabling more targeted early prevention and treatment interventions.Correct data analysis is pivotal in making predictions. However, a common challenge often encountered is the imbalance in data classes, which can result in a predictive model being biased. This is certainly detrimental, especially in the context of predicting strokes, where prediction accuracy can mean the difference between life and death.In this research, our focus was on developing a Deep Neural Network (DNN) Architecture model. This model aims to offer more accurate predictions by considering data complexities. By optimizing several key parameters, such as the type of optimizer, learning rate, and the number of epochs, we strived to achieve the model's best performance. Specifically, we selected Adagrad as the optimizer, set the learning rate at 0.01, and employed a total of 100 epochs in its training.The results obtained from this research are quite promising. The optimized DNN model displayed an accuracy score of 0.92, precision of 0.92, recall of 0.95, and an f-measure of 0.93. This indicates that with the right approach and meticulous optimization, technology can be a highly valuable tool in combatting heart diseases.

Keywords

DNN, Deep Learning, Heart Disease, Classification

References

A. Mehmood et al., “Prediction of Heart Disease Using Deep Convolutional Neural Networks,” Arab. J. Sci. Eng., vol. 46, no. 4, pp. 3409–3422, 2021, doi: 10.1007/s13369-020-05105-1.

J. A. Ramirez-Bautista, A. Hernández-Zavala, S. L. Chaparro-Cárdenas, and J. A. Huerta-Ruelas, “Review on plantar data analysis for disease diagnosis,” Biocybern. Biomed. Eng., vol. 38, no. 2, pp. 342–361, 2018.

P. Balakumar, K. Maung-U, and G. Jagadeesh, “Prevalence and prevention of cardiovascular disease and diabetes mellitus,” Pharmacol. Res., vol. 113, pp. 600–609, 2016.

N. K. Kumar, G. S. Sindhu, D. K. Prashanthi, and A. S. Sulthana, “Analysis and prediction of cardio vascular disease using machine learning classifiers,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 15–21.

S. Islam, N. Jahan, and M. E. Khatun, “Cardiovascular disease forecast using machine learning paradigms,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 2020, pp. 487–490.

D. Zhang et al., “Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network,” J. Healthc. Eng., vol. 2021, no. Ml, 2021, doi: 10.1155/2021/6260022.

B. Dun, E. Wang, and S. Majumder, “Heart disease diagnosis on medical data using ensemble learning,” Comput. Sci, vol. 1, pp. 1–5, 2016.

G. Guidi, M. C. Pettenati, P. Melillo, and E. Iadanza, “A machine learning system to improve heart failure patient assistance,” IEEE J. Biomed. Heal. informatics, vol. 18, no. 6, pp. 1750–1756, 2014.

P. Jeatrakul, K. W. Wong, and C. C. Fung, “Using misclassification analysis for data cleaning,” in International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2009, 2009.

M. Seyednourani, “A robust process model with two-stage optimization methodology for liquid composite molding process,” 2020.

J. T. Hancock and T. M. Khoshgoftaar, “Survey on categorical data for neural networks,” J. Big Data, vol. 7, no. 1, pp. 1–41, 2020.

D. K. Thara, B. G. PremaSudha, and F. Xiong, “Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques,” Pattern Recognit. Lett., vol. 128, pp. 544–550, 2019.

W. S. E. Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.

I. Alfarobi, T. A. Tutupoly, and A. Suryanto, “Komparasi Algoritma C4.5, Naive Bayes Dan Random Forest Untuk Klasifikasi Data Kelulusan Mahasiswa Jakarta,” Mitra dan Teknol. Pendidik, vol. IV, no. 1, pp. 1–14, 2018.

I. Mackie, Introduction to Deep Learning. Springer, 2018. doi: 10.1007/978-3-319-73004-2.

T. Djatna, M. K. D. Hardhienata, and A. F. N. Masruriyah, “An Intuitionistic Fuzzy Diagnosis Analytics for Stroke Disease,” J. Big Data, pp. 1–14, 2018, doi: 10.1186/s40537-018-0142-7.

M. N. Nasir and I. Budiman, “Perbandingan Pengaruh Nilai Centroid Awal Pada Algoritma K-Means Dan K-Means ++ Confusion Matrix,” Semin. Nas. Ilmu Komput., vol. 1, pp. 118–127, 2017.

Article Metrics

Abstract view: 124 times
Download     : 89   times Download     : 24   times

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

  • There are currently no refbacks.