A Systematic Comparative Study on the use of Machine Learning Techniques to Predict Lung Cancer and its Metastasis to the Liver: LCLM-Predictor Model

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 11, 2025

Lung cancer is one of the major causes deaths with thousands affected patients who have developed liver metastasis, complicating treatment and further prognosis. Early predictions lung metastasis may greatly improve patient outcomes since clinical interventions will be instituted in time. This paper compares performance different machine learning models including Decision Tree Classifiers, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Support Vector Machines Gaussian Mixture Models toward best set techniques for prediction. The applied dataset includes various features, such as respiratory symptoms biochemical markers, development stronger predictive performance. were cross-validated using testing validation aimed at generalizing whole model reliability generating both train test data. results generated are gauged metrics accuracy, precision, recall, F1-score, area under ROC curve. Results obtained revealed that KNN also showed accuracy strong classification performance, especially early-stage metastasis. present study a comparison models, which hence denotes potential these decision-making suggests application to diagnostic tools early detection cancer. provides very useful guide applicable use oncology helps pave way future research would focused on optimization integration into healthcare systems produce better management survival rates.

Language: Английский

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Social and Cognitive Predictors of Collaborative Learning in Music Ensembles

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 13, 2025

There have been many attempts to find ways make music education more relevant and useful for pupils. Learning theories, performance-based learning, contract-learning, discovery-learning, cooperative daily clocking, stage practice, music-focused required elective courses are all part of the implementation these methods. Since high vocational students tend lower GPAs, it is imperative that they discover strategies enhance their academic performance. Reform, rather than relying on theoretical frameworks, should be grounded practical, innovative human actions. Both instructors pupils possess capacity comprehend what learnt, according humanistic perspective. This paper provides evidence collaborative learning beneficial first-year practice in a popular program at Chinese institution. The work small, diverse groups. Data was collected analyzed from over course one year with grades 4-6.. Collaboration powerful tool has applications, including but not limited degree programs, which implemented this using machine techniques. It zeroed down seven important characteristics, had obvious applications educational process. Another online could use method predict students' performance, real-time tracking progress risk dropping out, after adjusted capture features corresponding different contexts. also applied other management platforms.

Language: Английский

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Understanding and Analysing Causal Relations through Modelling using Causal Machine Learning

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 11, 2025

The study of causal inference has gained significant attention in artificial intelligence (AI) and machine learning (ML), particularly areas such as explainability, automated diagnostics, reinforcement learning, transfer learning.. This research applies techniques to analyze student placement data, aiming establish cause-and-effect relationships rather than mere correlations. Using the DoWhy Python library, follows a structured four-step approach—Modeling, Identification, Estimation, Refutation—and introduces novel 3D framework (Data Correlation, Causal Discovery, Domain Knowledge) enhance modeling reliability. discovery algorithms, including Peter Clark (PC), Greedy Equivalence Search (GES), Linear Non-Gaussian Acyclic Model (LiNGAM), are applied construct validate robust model. Results indicate that internships (0.155) academic branch selection (0.148) most influential factors placements, while CGPA (0.042), projects (0.035), employability skills (0.016) have moderate effects, extracurricular activities (0.004) MOOCs courses (0.012) exhibit minimal impact. underscores significance reasoning higher education analytics highlights effectiveness ML real-world decision-making. Future work may explore larger datasets, integrate additional educational variables, extend this approach other disciplines for broader applicability.

Language: Английский

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AI-Driven Heart Disease Prediction Using Machine Learning and Deep Learning Techniques

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 10, 2025

Heart disease remains a leading cause of mortality worldwide, necessitating early detection and prevention strategies. This study explores machine learning (ML) approaches for predicting heart using patient datasets. Various ML algorithms, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, XGBoost, an Artificial Neural Network (ANN), were implemented to classify presence. The Forest model achieved the highest accuracy 95%. findings demonstrate that can significantly enhance prediction, aiding diagnosis treatment.

Language: Английский

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Towards Precision Medicine with Genomics using Big Data Analytics

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 23, 2025

Precision medicine is considered to be the future of healthcare. It allows doctors select treatments based on patient's genetic information. being adapted a few typical complicated like cancer at an intermediate level. As information in large volumes, Big data analytics showing reliable promise modern-day health care revolution. Extremely and continuous collection volumes Genomics, Proteomics, Glycomics etc. creating challenge analysis interpretation, which addressed effectively by analytics. This research work reviews highlights evolution medicine, Data Analytics its significance related work. Also detailed Machine learning perspectives Precise with genomic models along Challenges.

Language: Английский

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Deep Learning Based Automated Detection of Arcus Senilis and Its Clinical Risks in Ocular Health

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 10, 2025

Arcus Senilis is a clinical indicator of lipid deposition in the cornea, commonly observed aging individuals. This study aims to develop an automated deep learning-based pipeline for detecting and estimating cholesterol levels from ocular images. We implemented image-based classification system using EfficientNetB0, state-of-the-art convolutional neural network (CNN). The dataset was pre-processed Contrast Limited Adaptive Histogram Equalization (CLAHE) enhance contrast. model trained transfer learning, incorporating global average pooling fully connected layers classify presence estimate levels. Additionally, patient metadata, including age levels, integrated prediction accuracy. on labelled dataset, with multi-task learning approach handling both (Arcus detection) regression (cholesterol level estimation). Performance evaluated Mean Absolute Error (MAE), R² Score, Accuracy, Confusion Matrices. proposed achieved accuracy 92.5% detection (MAE) 8.4 mg/dL estimation. effectively distinguished normal eyes provided clinically relevant estimations. Evaluation metrics, precision, recall, F1-score, demonstrated its reliability compared traditional machine approaches such as SVM + HOG Features, ResNet50, VGG16. provides non-invasive, accurate, solution findings suggest potential applications ophthalmic diagnostics metabolism assessment.

Language: Английский

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