Bidang B - 2023-2024 Ganjil (Combatting Heart Diseases: Advanced Predictions Using Optimized DNN Architecture)

Bidang B - 2023-2024 Ganjil (Combatting Heart Diseases: Advanced Predictions Using Optimized DNN Architecture)

• By Azis, Mochammad Abdul; Sumarna, Sumarna;
• Jurnal Nasional Terakreditasi
• 192
• 117-125

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, and 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, a precision of 0.92, a 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.

• Katakunci DNN Deep Learning Heart Disease Classification
• URL https://ejournals.itda.ac.id/index.php/compiler/article/view/1915/pdf_1
• Penerbit ,

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