An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets

Khalaf, Mohammed; Hussain, Abir Jaafar; Alafandi, Omar; Al‐Jumeily, Dhiya; Alloghani, Mohamed; Alsaadi, Mahmood; Dawood, Omar A.; Hamed, Dhafar

doi:10.1007/978-3-030-26969-2_55

Cited by 15 publications

(5 citation statements)

References 24 publications

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“…The results of the study showed that the activation of the Hippo/YAP pathway was associated with different prognostic and clinical features, and could be used to guide personalized treatment strategies for LGG. The use of machine learning models in many aspects has been gaining increasing attention in recent years (28)(29)(30). We used a composite machine learning model to predict individual patients' survival risk profiles and the Hippo/YAP pathway status.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

et al. 2023

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BackgroundThe activation of YAP/TAZ transcriptional co-activators, downstream effectors of the Hippo/YAP pathway, is commonly observed in human cancers, promoting tumor growth and invasion. The aim of this study was to use machine learning models and molecular map based on the Hippo/YAP pathway to explore the prognosis, immune microenvironment and therapeutic regimen of patients with lower grade glioma (LGG).MethodsSW1783 and SW1088 cell lines were used as in vitro models for LGG, and the cell viability of the XMU-MP-1 (a small molecule inhibitor of the Hippo signaling pathway) treated group was evaluated using a Cell Counting Kit-8 (CCK-8). Univariate Cox analysis on 19 Hippo/YAP pathway related genes (HPRGs) was performed to identify 16 HPRGs that exhibited significant prognostic value in meta cohort. Consensus clustering algorithm was used to classify the meta cohort into three molecular subtypes associated with Hippo/YAP Pathway activation profiles. The Hippo/YAP pathway’s potential for guiding therapeutic interventions was also investigated by evaluating the efficacy of small molecule inhibitors. Finally, a composite machine learning models was used to predict individual patients’ survival risk profiles and the Hippo/YAP pathway status.ResultsThe findings showed that XMU-MP-1 significantly enhanced the proliferation of LGG cells. Different Hippo/YAP Pathway activation profiles were associated with different prognostic and clinical features. The immune scores of subtype B were dominated by MDSC and Treg cells, which are known to have immunosuppressive effects. Gene Set Variation Analysis (GSVA) indicated that subtypes B with a poor prognosis exhibited decreased propanoate metabolic activity and suppressed Hippo pathway signaling. Subtype B had the lowest IC50 value, indicating sensitivity to drugs that target the Hippo/YAP pathway. Finally, the random forest tree model predicted the Hippo/YAP pathway status in patients with different survival risk profiles.ConclusionsThis study demonstrates the significance of the Hippo/YAP pathway in predicting the prognosis of patients with LGG. The different Hippo/YAP Pathway activation profiles associated with different prognostic and clinical features suggest the potential for personalized treatments.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

et al. 2023

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“…The parameter optimization strategy was employed to the patient clinical data model using Support Vector Machine (SVM) [19]. It is among the promising classifiers in healthcare data analytics.…”

Section: Support Vector Machinementioning

confidence: 99%

An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

Gollapalli¹,

Alfaleh²

2022

MMEP

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Sickle cell disease (SCD) is a genetic illness that affects red blood cells and can lead to major complications like Acute Chest Syndrome (ACS), Cerebrovascular Accident (CVA), and even death from a stroke. SCD is frequent in some places of the world, especially where consanguineous marriages are common, such as in Saudi Arabia. This research presents artificial intelligence (AI) models that were tested on hospital clinical data collected between 2008 to 2020 using five different classifiers: Naive Bayes, Neural Networks (NN), Support Vector Machine (SVM), J48, and PART. To select the optimum classification approach, we analyzed the models based on the accuracy kappa statistics and the classification time. We also compared performance criteria such as sensitivity, specificity, accuracy, F1 measure, and AUC. The naive Bayes classifier outperformed the other classifiers with 92.22% accuracy in our investigation, which was then utilized to determine key elements that are common between SCD patients' inheritance and demographic data. The findings of this study through AI are aimed to assist hospital doctors and practitioners understand the correlation between disease inheritance and other factors, allowing them to better manage the disease by increasing disease awareness in the community, particularly among mothers. In terms of accuracy, all classifiers obtained above 92%. J48, NN and SVM exhibited 92.99% accuracy, then PART exhibits accuracy as 92.92% followed by Naï ve Bayes at 92.22%. In terms of classification time, Naï ve Bayes was fastest (0.01 sec), then J48 (0.03 sec), then PART (0.24 sec) and SVM (0.28 sec) while NN was slowest at 22.43 sec.

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“…The curve shape comes out when the RBF function moves away from the support-vector (center). It is provides an opportunity to draw hyperplane that aptly separate points [48]. It can calculated by equation 11…”

Section: Second Featurementioning

confidence: 99%

SWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis

Al-Salihi¹,

Aydin²,

Ghaeb³

2021

Turk J Elec Eng & Comp Sci

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A human cornea is the front see-through shield of the eye; that refracts light onto the retina to induce the vision. Therefore, any defect in the cornea should lead to vision disturbance. This deficiency is estimated by sets of topographical images measured assessed by an ophthalmologist. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images produced by a Pentacam device can be subjected to rotation or some distortion during acquisition, therefore, accurate diagnosis requires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for local features transform (SWFT) developed which mainly based on the algorithm of a scale-invariant feature transform (SIFT). This algorithm uses wavelets as a multiresolution analysis to produce images with different scales instead of using the Difference of Gaussians (DoG) as in the SIFT algorithm. The Experimental results on the corneal topography dataset, indicate that the proposed SWFT outperforms the baseline SIFT algorithm.

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An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets

Cited by 15 publications

References 24 publications

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

SWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis

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