Prediction of Complete Remission and Survival in Acute Myeloid Leukemia Using Supervised Machine Learning

Röllig, Christoph; Kramer, Michael; Stasik, Sebastian; Georgi, Julia-Annabell; Heisig, Peter; Kroschinsky, Frank; Schetelig, Johannes; Platzbecker, Uwe; Müller‐Tidow, Carsten; Sauer, Tim; Baldus, Claudia D.; Schaefer‐Eckart, Kerstin; Kaufmann, Martin; Krause, Stefan W.; Haenel, Mathias; Schliemann, Christoph; Hanoun, Maher; Thiede, Christian; Bornhaeuser, Martin; Wendt, Karsten; Middeke, Jan Moritz

doi:10.1182/blood-2021-149582

Cited by 3 publications

(4 citation statements)

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“…The results of our study are consistent with the studies by Eckardt et al[ 28 ], Coombes et al [ 41 ], and Orgueira et al [ 14 ] In these studies, after implementing various ML models to predict the survival of patients with leukemia, the SVM classifier yielded the best performance. In a study by Karami et al, the SVM had better performance with 85.17% accuracy and 0.93% AUC for the survival prognosis of AML patients [ 30 ].…”

Section: Discussionsupporting

confidence: 92%

“…Previous studies showed that numerous clinical and non-clinical predictors influence CML survival. In reviewed studies, after performing feature selection, a number of demographical and clinical manifestation variables such as age [ 27 , 31 , 39 , 40 ], sex [ 14 , 27 , 40 ], body mass index (BMI) [ 10 , 27 , 30 , 31 ], race [ 27 , 40 ], body pain [ 14 , 25 , 27 , 28 , 40 , 41 ], general malaise [ 10 , 14 , 27 , 31 , 40 ], fever [ 10 , 26 , 28 – 30 ], night sweats [ 25 , 28 – 30 , 41 ], unexplained hemorrhage [ 10 , 26 , 28 , 30 , 39 ], general infection [ 14 , 31 , 40 ], enlarged spleen [ 10 , 14 , 27 , 31 , 39 , 40 ], cachexia [ 25 , 27 , 30 , 31 ], anorexia [ 10 , 14 , 29 , 31 ], and drug resistance [ 25 , 27 – 29 , 41 ] are determined as the most important predictors affecting CML survival. Besides, neutrophil/lymphocyte count [ 10 , 14 , 25 – 27 , 29 , 31 , 39 ], lactate dehydrogenase (LDH)...…”

Section: Discussionmentioning

confidence: 99%

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Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia

Shanbehzadeh

Afrash

Mirani

et al. 2022

BMC Med Inform Decis Mak

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Introduction Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes 15–20% of all cases of leukemia. Although bone marrow transplant and, more recently, tyrosine kinase inhibitors (TKIs) as a first-line treatment have significantly prolonged survival in CML patients, accurate prediction using available patient-level factors can be challenging. We intended to predict 5-year survival among CML patients via eight machine learning (ML) algorithms and compare their performance. Methods The data of 837 CML patients were retrospectively extracted and randomly split into training and test segments (70:30 ratio). The outcome variable was 5-year survival with potential values of alive or deceased. The dataset for the full features and important features selected by minimal redundancy maximal relevance (mRMR) feature selection were fed into eight ML techniques, including eXtreme gradient boosting (XGBoost), multilayer perceptron (MLP), pattern recognition network, k-nearest neighborhood (KNN), probabilistic neural network, support vector machine (SVM) (kernel = linear), SVM (kernel = RBF), and J-48. The scikit-learn library in Python was used to implement the models. Finally, the performance of the developed models was measured using some evaluation criteria with 95% confidence intervals (CI). Results Spleen palpable, age, and unexplained hemorrhage were identified as the top three effective features affecting CML 5-year survival. The performance of ML models using the selected-features was superior to that of the full-features dataset. Among the eight ML algorithms, SVM (kernel = RBF) had the best performance in tenfold cross-validation with an accuracy of 85.7%, specificity of 85%, sensitivity of 86%, F-measure of 87%, kappa statistic of 86.1%, and area under the curve (AUC) of 85% for the selected-features. Using the full-features dataset yielded an accuracy of 69.7%, specificity of 69.1%, sensitivity of 71.3%, F-measure of 72%, kappa statistic of 75.2%, and AUC of 70.1%. Conclusions Accurate prediction of the survival likelihood of CML patients can inform caregivers to promote patient prognostication and choose the best possible treatment path. While external validation is required, our developed models will offer customized treatment and may guide the prescription of personalized medicine for CML patients.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia

Shanbehzadeh

Afrash

Mirani

et al. 2022

BMC Med Inform Decis Mak

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“…These limitations stem from the reuse of the same subjects, contrasting with the synthesis of new data in approaches like VAEs (or other generative AI algorithms). Moreover, AI-synthetic data have already been used to mimic clinical trials in patients with leukemia and other healthcare applications [50][51][52][53][54]. Notably, tech giants like Amazon (for Alexa) and Google (for self-driving cars), as well as pharmaceutical companies like Roche, utilize synthetic data to train their systems.…”

Section: Discussionmentioning

confidence: 99%

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

Papadopoulos,

Karalis

2024

Applied Sciences

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Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample is necessary to gain valuable insights into a designated population. However, large sample sizes lead to increased human exposure, costs, and a longer time for completion. In a previous study, we introduced the idea of using variational autoencoders (VAEs), a type of artificial neural network, to synthetically create in clinical studies. In this work, we further elaborate on this idea and expand it in the field of bioequivalence (BE) studies. A computational methodology was developed, combining Monte Carlo simulations of 2 × 2 crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, the actual sample size, the VAE-generated sample size, and the difference in performance between the two pharmaceutical products under comparison, were explored. All simulations showed that incorporating AI generative algorithms for creating virtual populations in BE trials has many advantages, as less actual human data can be used to achieve similar, and even better, results. Overall, this work shows how the application of generative AI algorithms, like VAEs, in clinical/bioequivalence studies can be a modern tool to significantly reduce human exposure, costs, and trial completion time.

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“…This application process often incorporates various algorithm optimizations to achieve high accuracy. Classification, one of the data mining methods for predicting specific classes [21], [22], has proven effective in the early detection and accurate diagnosis of disease including glaucoma [23], [24], brain tumors [25]- [27], acute lymphoblastic leukemia [28], [29], and PD [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Jumanto,

Rofik,

Sugiharti

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics. Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

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Prediction of Complete Remission and Survival in Acute Myeloid Leukemia Using Supervised Machine Learning

Cited by 3 publications

References 0 publications

Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia

Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

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