Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach

Camerlingo, Nunzio; Vettoretti, Martina; Favero, Simone Del; Facchinetti, Andrea; Choudhary, Pratik; Sparacino, Giovanni

doi:10.1016/j.cmpb.2022.106862

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Ketujuh, adanya bias pemilihan variabel dan metode analisis serta interpretasi yang subyektif juga harus diperhatikan (Cembranel et al, 2023). Kedelapan, faktor perubahan sosial dan budaya turut memengaruhi relevansi faktor-faktor yang ditemukan (Camerlingo et al, 2022). Kesembilan, walaupun faktorfaktor dapat teridentifikasi, merumuskan intervensi atau tindakan yang tepat memerlukan penelitian lanjutan (M. Li et al, 2022).…”

Section: Pendahuluanunclassified

Exploring Complex Decision Trees: Unveiling Data Patterns and Optimal Predictive Power

Setiawan,

Fina Antika Cahyani,

Sadida

2023

j.innov.future.technology

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This research investigates the development and analysis of decision tree models in the context of classification tasks. Decision tree models were developed without employing pruning or pre-pruning techniques and were tested on relevant datasets. The research findings demonstrate that complex models without pruning achieved the highest level of accuracy in classifying data. This study was inspired by the potential issue of students facing the risk of not completing their studies (dropout), which could lead to a decline in the college's accreditation rating. Therefore, this model was devised to assist in identifying factors that could influence this outcome as a preventative measure. Additionally, we successfully generated clear visualizations of the decision trees, enhancing the understanding of the model's decision-making process. This research provides insights into the adaptability of decision tree models within this specific case and showcases their potential for enhancing decision-making across various contexts. These findings encourage further discussions on the benefits of pruning methods within this specific context and the broader application potential of decision tree models.

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Section: Pendahuluanunclassified

Exploring Complex Decision Trees: Unveiling Data Patterns and Optimal Predictive Power

Setiawan,

Fina Antika Cahyani,

Sadida

2023

j.innov.future.technology

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“…Diabetes mellitus, yang umumnya dikenal sebagai diabetes, merupakan salah satu penyakit kronis yang mempengaruhi jutaan orang di seluruh dunia (Camerlingo et al, 2022;Sunanto & Falah, 2022). Penyakit ini ditandai dengan peningkatan kadar gula darah yang terjadi akibat ketidakmampuan pankreas untuk memproduksi atau memanfaatkan insulin dengan efektif (Ente et al, 2020;Karyadiputra & Setiawan, 2022).…”

Section: Pendahuluanunclassified

Klasifikasi Penyakit Diabetes Menggunakan Algoritma Decision Tree

Nurussakinah,

Faisal

2023

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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Penyakit diabetes merupakan salah satu penyakit kronis yang prevalensinya terus meningkat di seluruh dunia. Diagnosis dini menjadi permasalahan utama dalam pengelolaan penyakit diabetes di mana banyak pasien tidak menyadari penyakit mereka sampai gejala serius muncul. Klasifikasi penyakit diabetes membantu mengidentifikasi diabetes pada tahap awal untuk intervensi yang lebih efektif. Penelitian ini menggunakan algoritma Decision Tree untuk mengidentifikasi serta mengklasisfikasikan pasien diabetes. Dataset bersal dari kumpulan data klinis pasien diabetes dan membangun model klasifikasi menggunakan algoritma Decision Tree. Model yang dihasilkan mampu mengidentifikasi pasien diabetes dengan tingkat akurasi yang tinggi berdasarkan data klinis yang terkumpul. Hasil evaluasi model menunjukkan presisi sebesar 0.78, recall sebesar 0.45, dan F1-score sebesar 0.57 untuk kelas positif diabetes. Namun, hasil evaluasi juga menunjukkan adanya batasan pada kinerja model dalam mengklasifikasikan pasien diabetes dengan recall yang lebih rendah. Oleh karena itu, penelitian ini memberikan landasan untuk pengembangan lebih lanjut guna meningkatkan performa model klasifikasi ini, seperti dengan mempertimbangkan atribut klinis lainnya yang relevan.

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“…Stroke and Diabetes mellitus (DM) are non-communicable diseases whose prevalence increases every year. DM is a chronic disease that affects many people throughout the world [2]. DM is characterized by an increase in blood glucose levels due to the inability of the pancreas to produce insulin effectively [3].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Stroke and Diabetes Mellitus With Classification Techniques Using Decision Tree Method

Pratama,

Suryana,

Alfiansyah

et al. 2024

IJHIS

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: Stroke is a cerebral vascular disease characterized by the death of brain tissue that occurs due to reduced blood and oxygen flow to the brain. Ischemic stroke is associated with diabetes mellitus, therefore it is important to identify the risk factors that cause stroke and DM by diagnostic cause of the disease. This study aimed to classify and compare accuracy tests on medical record data sets for stroke and DM. This study analyzed the diagnosis of stroke and DM using Decision Tree. The risk factors consisted of gender, age, blood pressure, nutritional status, smoking, history of DM, and history of hypertension. The results of the analysis using the Decision Tree method showed that the accuracy rate was 86.67%, which means that the modeling has a good level of correctness of the prediction results. We conclude that the Decision Tree method was an accurate method for detecting stroke and DM.

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Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach

Cited by 5 publications

References 40 publications

Exploring Complex Decision Trees: Unveiling Data Patterns and Optimal Predictive Power

Exploring Complex Decision Trees: Unveiling Data Patterns and Optimal Predictive Power

Klasifikasi Penyakit Diabetes Menggunakan Algoritma Decision Tree

Diagnosis of Stroke and Diabetes Mellitus With Classification Techniques Using Decision Tree Method

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