scholarly journals A Comparative Analysis of Machine Learning Algorithms to Build a Predictive Model for Detecting Diabetes Complications

Informatica ◽  
2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Ali A. Abaker ◽  
Fakhreldeen A. Saeed
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Predictive Model ◽  
Diabetes Complications ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Comparative analysis of machine learning algorithms for prediction of smart grid stability †

2021 ◽  
Author(s):  
Ali Kashif Bashir ◽  
Suleman Khan ◽  
B Prabadevi ◽  
N Deepa ◽  
Waleed S. Alnumay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

2021 ◽  
Vol 17 ◽  
Author(s):  
Hui Zhang ◽  
Qidong Liu ◽  
Xiaoru Sun ◽  
Yaru Xu ◽  
Yiling Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Predictive Model ◽  
Differentially Expressed Genes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Diagnosis And Treatment ◽  
Ppi Network ◽  
Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

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