Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches

Ahamad, Md. Martuza; Aktar, Sakifa; Uddin, Md. Jamal; Rahman, Tasnia; Alyami, Salem A.; Al-Ashhab, Samer; Akhdar, Hanan; Azad, A. K. M.; Moni, Mohammad Ali

doi:10.3390/jpm12081211

Cited by 37 publications

(17 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its notable capabilities include the adept handling of extensive datasets and the achievement of high-accuracy outcomes while operating within constraints of computing resources, such as memory space and computing speed. This proficiency positions LGBM as a favorable model when compared to alternative approaches [ 33 ]. Kim H. et al used LGBM to classify early-stage laryngeal cancer by classification of voice change.…”

Section: Discussionmentioning

confidence: 99%

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model

Mun,

Tran,

Park

et al. 2024

Bioengineering

View full text Add to dashboard Cite

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model

Mun,

Tran,

Park

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…LightGBM has been used for the prediction of neurological prognoses after cervical spinal cord injuries [ 22 ]. The algorithm has been used to detect early stages of ovarian cancer or bladder cancer using blood test and tumor marker data [ 23 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach

et al. 2023

View full text Add to dashboard Cite

The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research.

show abstract

“…It is calculated as the ratio of the summation of the true positive (TPS) and true negative (TNG) in the whole population [60]. Precision is the percentage of predicted positives that are real [61]. Recall measures the models ability to categorize samples inside a class [62].…”

Section: Model Evaluationmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

et al. 2023

Self Cite

View full text Add to dashboard Cite

Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

show abstract

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches

Cited by 37 publications

References 28 publications

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model

Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

Contact Info

Product

Resources

About