Optimasi Fungsi Aktivasi pada Artificial Neural Network untuk Prediksi Gagal Jantung Secara Akurat

Mokhamad Ramdhani Raharjo; Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar; Putrama Alkhairi

doi:10.47065/explorer.v5i1.1840

Mokhamad Ramdhani Raharjo * Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, Indonesia
Indra Riyana Rahadjeng Universitas Bina Sarana Informatika, Indonesia
Muhammad Noor Hasan Siregar Universitas Graha Nusantara, Padang Sidempuan, Indonesia
Putrama Alkhairi STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI: https://doi.org/10.47065/explorer.v5i1.1840

Keywords: Activation Function Optimization; ANN; Heart Failure Prediction; Optimization

Abstract

Heart failure is one of the major health problems that can be fatal if not diagnosed properly and quickly. Therefore, early prediction using artificial intelligence models, especially Artificial Neural Network (ANN), is needed to improve the accuracy in detecting heart failure. This study aims to optimize the activation function in ANN to predict heart failure accurately. Several optimization algorithms tested, namely Adam, RMSprop, SGD, Adagrad, and Adadelta, were used to evaluate model performance in terms of accuracy, precision, recall, and F1-score. The results showed that the Adam optimization algorithm provided the best performance with an accuracy of 86.74%, precision of 75.12%, recall of 66.67%, and F1-score of 70.64%. Meanwhile, other algorithms such as RMSprop, SGD, Adagrad, and Adadelta showed lower performance, with some metrics reaching 0%. This study shows that proper activation function optimization in ANN is very important to improve the model's ability to predict heart failure with a high level of accuracy.

References

A. Jasinska-Piadlo et al., “Data-driven versus a domain-Led Approach To K-Means Clustering On An Open Heart Failure Dataset,” Int. J. Data Sci. Anal., Vol. 15, No. 1, Pp. 49–66, 2023, Doi: 10.1007/S41060-022-00346-9.

Y. Yang, “A Self-Powered Laminated Fabric Sensor For Human Motion Detection And Heart-Rate Monitoring Based On Ppy/Al Schottky Contact,” J. Sandw. Struct. Mater., Vol. 24, No. 1, Pp. 503–516, 2022, Doi: 10.1177/10996362211021889.

Z. A. Sejuti And M. S. Islam, “A Hybrid CNN–KNN Approach For Identification Of COVID-19 With 5-Fold Cross Validation,” Sensors Int., Vol. 4, No. November 2022, P. 100229, 2023, Doi: 10.1016/J.Sintl.2023.100229.

N. A. M. Zaini And M. K. Awang, “Performance Comparison Between Meta-Classifier Algorithms For Heart Disease Classification,” Int. J. Adv. Comput. Sci. Appl., Vol. 13, No. 10, Pp. 323–328, 2022, Doi: 10.14569/IJACSA.2022.0131039.

G. Berra, “Phenotyping CLAD After Single Lung Transplant: Limits And Prognostic Assessment Of The 2019 ISHLT Classification System,” J. Hear. Lung Transplant., Vol. 41, No. 5, Pp. 599–607, 2022, Doi: 10.1016/J.Healun.2022.01.015.

Saiyedfaiayazwaris And S. Koteeswaran, “Early Prediction Of Heart Conditions By K-Means Consensus Clustering And Convolution Neural Network,” Vol. 25, No. 3, Pp. 6623–6640, 2021, [Online]. Available: Http://Annalsofrscb.Ro

Y. Deng Et Al., “New Methods Based On Back Propagation (BP) And Radial Basis Function (RBF) Artificial Neural Networks (Anns) For Predicting The Occurrence Of Haloketones In Tap Water,” Sci. Total Environ., Vol. 772, P. 145534, 2021, Doi: 10.1016/J.Scitotenv.2021.145534.

K. G. Ravindra, B. M. Rudresh, P. K. MK, And P. Kumar, “Optimization Of Design Parameters To Evaluate The Performance Of Journal Bearing: ANN Approach,” Mater. Today …, 2022, [Online]. Available: Https://Www.Sciencedirect.Com/Science/Article/Pii/S2214785321056431

M. A. Lubis, D. G. S. Saragih, I. D. Anastasia, A. P. Windarto, And P. Alkhairi, “Application Of The ANN Algorithm To Predict Access To Drinkable Water In North Sumatra Regency/City,” Int. J. Informatics Data Sci., Vol. 1, No. 1, Pp. 18–25, 2023.

A. S. Parihar, “Dehazing Optically Haze Images With Alexnet-FNN,” J. Opt., Vol. 53, No. 1, Pp. 294–303, 2024, Doi: 10.1007/S12596-023-01156-3.

N. A. Hamid, N. M. Nawi, R. Ghazali, And M. N. M. Salleh, “Accelerating Learning Performance Of Back Propagation Algorithm By Using Adaptive Gain Together With Adaptive Momentum And Adaptive Learning Rate On Classification Problems,” Int. J. Softw. Eng. Its Appl., Vol. 5, No. 4, Pp. 31–44, 2011.

M. Chu, P. Wu, G. Li, W. Yang, J. L. Gutiérrez-Chico, And S. Tu, “Advances In Diagnosis, Therapy, And Prognosis Of Coronary Artery Disease Powered By Deep Learning Algorithms,” JACC Asia, Vol. 3, No. 1, Pp. 1–14, 2023, Doi: 10.1016/J.Jacasi.2022.12.005.

Q. Hou, R. Xia, J. Zhang, Y. Feng, Z. Zhan, And X. Wang, “Learning Visual Overlapping Image Pairs For Sfm Via CNN Fine-Tuning With Photogrammetric Geometry Information,” Int. J. Appl. Earth Obs. Geoinf., Vol. 116, No. October 2022, P. 103162, 2023, Doi: 10.1016/J.Jag.2022.103162.

K. E. Arunkumar, “Comparative Analysis Of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) Cells, Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) For Forecasting COVID-19 Trends,” Alexandria Eng. J., Vol. 61, No. 10, Pp. 7585–7603, 2022, Doi: 10.1016/J.Aej.2022.01.011.

A. Narula, “Development Of CNN-LSTM Combinational Architecture For COVID-19 Detection,” J. Ambient Intell. Humaniz. Comput., Vol. 14, No. 3, Pp. 2645–2656, 2023, Doi: 10.1007/S12652-022-04508-2.

N. Lopes And B. Ribeiro, “Towards Adaptive Learning With Improved Convergence Of Deep Belief Networks On Graphics Processing Units,” Pattern Recognit., Vol. 47, No. 1, Pp. 114–127, 2014, Doi: 10.1016/J.Patcog.2013.06.029.

S. Kamada, T. Ichimura, A. Hara, And K. J. Mackin, “Adaptive Structure Learning Method Of Deep Belief Network Using Neuron Generation – Annihilation And Layer Generation,” Neural Comput. Appl., Vol. 0123456789, 2018, Doi: 10.1007/S00521-018-3622-Y.

S. A. Ebiaredoh-Mienye, E. Esenogho, And T. G. Swart, “Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique And Softmax Regression For Medical Diagnosis,” 2020.

I. Irvanizam, “Applying Artificial Neural Network Based On Backpropagation Method For Indonesian Sign Language Recognition,” Int. J. Comput. Digit. Syst., Vol. 14, No. 1, Pp. 975–985, 2023, Doi: 10.12785/Ijcds/140176.

R. N. Singarimbun, “Backpropagation Artificial Neural Network Enhancement Using Beale-Powell Approach Technique,” J. Phys. Conf. Ser., Vol. 2394, No. 1, 2022, Doi: 10.1088/1742-6596/2394/1/012007.

S. Defit, A. P. Windarto, And P. Alkhairi, “Comparative Analysis Of Classification Methods In Sentiment Analysis: The Impact Of Feature Selection And Ensemble Techniques Optimization,” Telematika, Vol. 17, No. 1, Pp. 52–67, 2024, Doi: 10.35671/Telematika.V17i1.2824.

P. Alkhairi, A. P. Windarto, And M. M. Efendi, “Optimasi LSTM Mengurangi Overfitting Untuk Klasifikasi Teks Menggunakan Kumpulan Data Ulasan Film Kaggle IMDB,” Vol. 6, No. 2, Pp. 1142–1150, 2024, Doi: 10.47065/Bits.V6i2.5850.

A. P. Windarto, I. R. Rahadjeng, M. N. H. Siregar, And P. Alkhairi, “Deep Learning To Extract Animal Images With The U-Net Model On The Use Of Pet Images,” J. MEDIA Inform. BUDIDARMA, Vol. 8, No. 1, Pp. 468–476, 2024, Doi: Http://Dx.Doi.Org/10.30865/Mib.V8i1.7199.

P. Alkhairi And A. P. Windarto, “Classification Analysis Of Back Propagation-Optimized CNN Performance In Image Processing,” J. Syst. Eng. Inf. Technol., Vol. 2, No. 1, Pp. 8–15, 2023, Doi: Https://Doi.Org/10.29207/Joseit.V2i1.5015.

M. Intraraprasit, “FILTER PRUNING BASED ON LOCAL GRADIENT ACTIVATION MAPPING IN CONVOLUTIONAL NEURAL NETWORKS,” Int. J. Innov. Comput. Inf. Control, Vol. 19, No. 6, Pp. 1697–1715, 2023, Doi: 10.24507/Ijicic.19.06.1697.

S. Sharma, S. Sharma, And A. Anidhya, “Understanding Activation Functions In Neural Networks,” Int. J. Eng. Appl. Sci. Technol., Vol. 4, No. 12, Pp. 310–316, 2020.

H. Pratiwi Et Al., “Sigmoid Activation Function In Selecting The Best Model Of Artificial Neural Networks,” J. Phys. Conf. Ser., Vol. 1471, No. 1, 2020, Doi: 10.1088/1742-6596/1471/1/012010.