scholarly journals A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Medicina ◽  
2021 ◽  
Vol 57 (2) ◽  
pp. 99
Author(s):  
Yueying Wang ◽  
Shuai Liu ◽  
Zhao Wang ◽  
Yusi Fan ◽  
Jingxuan Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Learning Algorithms ◽  
Primary Lung Cancer ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Cancer Type ◽  
Lung Cancers ◽  
One Year ◽  
Gender Specific

Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.

scholarly journals Patient Survival Prediction with Machine Learning Algorithms

2020 ◽  
pp. 93-96
Author(s):  
Mustafa Berkant Selek ◽  
Saadet Sena Egeli ◽  
Yalcin Isler
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Cross Validation ◽  
Patient Survival ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Accuracy Rate ◽  
Intensive Care Patients ◽  
One Year

In this study, the intensive care unit patient survival is predicted by machine learning algorithms according to the examinations performed in the first 24 hours. The data of intensive care patients collected from approximately two hundred hospitals over a period of one year were used. Algorithms are run in Python environment. Machine learning models were compared with the Cross-Validation method, and the random forest algorithm is used. The model made the prediction with 92,53% accuracy rate.

scholarly journals ClimateLearn: A machine-learning approach for climate prediction using network measures

2016 ◽  
Author(s):  
Qing Yi Feng ◽  
Ruggero Vasile ◽  
Marc Segond ◽  
Avi Gozolchiani ◽  
Yang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
El Niño ◽  
El Nino ◽  
Learning Algorithms ◽  
Climate Prediction ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Network Measures ◽  
Sea Surface Temperature Anomalies ◽  
One Year

Abstract. We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. The package allows basic operations of data mining, i.e. reading, merging, and cleaning data, and running machine learning algorithms such as multilayer artificial neural networks and symbolic regression with genetic programming. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific, first concentrating on the occurrence of El Niño events one year ahead and second on the evolution of sea surface temperature anomalies with a lead time of three months.

scholarly journals Analysis of Shape-Based and Texture-Based Attributes in Classification of Mammographic Findings by Machine Learning Algorithms

2015 ◽  
Author(s):  
Matheus De Melo ◽  
Andy Gajadhar ◽  
Hugo De Oliveira ◽  
Arnaldo De Andrade e Silva ◽  
Leonardo Batista
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Radial Basis Function Network ◽  
Machine Learning Algorithms ◽  
Cancer Type ◽  
Classification Rate ◽  
Mammographic Finding ◽  
Perceptron Algorithm ◽  
Test Database

Breast cancer is the most frequent cancer type among women. We present a method of classification of nodules (malignant or benign) found in mammograms using shape-based attributes and texture-based ones. Firstly, we built a test database, then we segmented and extracted a Gray Level Cooccurrence Matrix (GLCM) from each mammographic finding and analyzed texture-based and shape-based attributes. Finally, classification was performed through machine learning algorithms. Tests reached a maximum Correct Classification Rate (CCR) of 93.75%, when performed with the Radial Basis Function Network algorithm. The largest area under the ROC curve (AUC), 0.964, was achieved with the Multilayer Perceptron algorithm.

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