Machine Learning-Based Prediction of Thalassemia: A Review
Jun 15, 2024 · Focusing on studies from the last five years, this review highlighted significant technological advancements in ML, including the use of predictive modeling, image analysis, …
Predicting thalassemia using deep neural network based on red …
Mar 15, 2023 · In this paper, we used deep learning algorithms to predict thalassemia. The genetic test was used as the gold standard to form the output label of the DNN model. RBC …
Thalassemia Prediction using Machine Learning Approaches
Mar 31, 2022 · In this research, predicting the existence of Thalassemia with ML, an important part of AI has been proposed. Very popular ML algorithms have been implemented on the …
Predicting Thalassemia Using Feature Selection Techniques: A ...
We review, analyze, and summarize approaches that pre-process and extract features from datasets related to thalassemia and use machine-learning algorithms to diagnose, classify, …
Fuzzy‐Based Fusion Model for β‐Thalassemia Carriers Prediction Using ...
Mar 25, 2024 · In the training phase, the third layer of the model is the application layer, which predicts thalassemia sickness using four different machine learning algorithms: Logistics …
Machine Learning-Based Prediction of Thalassemia: A Review
Jun 15, 2024 · Focusing on studies from the last five years, this review highlighted significant technological advancements in ML, including the use of predictive modeling, image analysis, …
We applied various machine learning classifiers such as Logistic Regression (LR), Decision Tree, Support Vector Machine (SVM), Random Forest, and K- Nearest Neighbors (KNN), etc. to …
In this project, data from a medical dataset is utilized to develop a machine learning model capable of classifying thalassemia cases into different diagnostic categories. The dataset …
An Application of Machine Learning to Thalassemia Diagnosis
Experimental results show that the prediction accuracy of the PCA-LR model is 87.5% (degree = 2, λ =4), and the prediction accuracy of the PLS model is 92.5% (ncomp = 4), indicating good …
Applications of Artificial Intelligence in Thalassemia: A …
There are different machine learning algorithms that are used to diagnose thalassemia using complete blood count (CBC) parameters with high sensitivity and specificity values.
- Some results have been removed