AIMC Topic: Support Vector Machine

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Enhancing Parkinson's disease prediction using meta-heuristic optimized machine learning models.

Personalized medicine
Parkinson's disease is a progressive neurological disorder affecting movement and cognition. Early detection is crucial but challenging with traditional methods. This study applies meta-heuristic optimization to enhance machine learning prediction mo...

Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.

PloS one
The increasing demand for natural medicine has increased the significance of Silybum marianum as a valuable medicinal plant. It is used to restore liver cells; reduce blood cholesterol; prevent prostate, skin, and breast cancer; and protect cervical ...

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Scientific reports
Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i.e., chemotherapy, hormone therapy, surgery, and radiation therapy is an essential step towards perso...

Model-driven multivariate control chart and support vector machine as tools to detect variation in the milking process and monitor parlor performance.

Journal of dairy science
The efficiency of the milking process is the key to dairy farm management. However, due to the high variability of data from single or multiple milk meters, it is difficult to know whether the milking process is under control or not. The main objecti...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

Surgical endoscopy
BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models for accurate prognostic predictions are lacking. We aimed to utilize Cox regression and integrate va...

Effectiveness of machine learning models in diagnosis of heart disease: a comparative study.

Scientific reports
The precise diagnosis of heart disease represents a significant obstacle within the medical field, demanding the implementation of advanced diagnostic instruments and methodologies. This article conducts an extensive examination of the efficacy of di...

Application of multimodal machine learning-based analysis for the biomethane yields of NaOH-pretreated biomass.

Scientific reports
This study investigated the impact of alkaline pretreatment on the biomethane yield of Xyris capensis experimentally and computationally using machine-learning (ML)-based techniques. Despite extensive studies on the anaerobic digestion of lignocellul...

Integrating Machine Learning into Myositis Research: a Systematic Review.

Clinical reviews in allergy & immunology
Idiopathic inflammatory myopathies (IIM) are a group of autoimmune rheumatic diseases characterized by proximal muscle weakness and extra muscular manifestations. Since 1975, these IIM have been classified into different clinical phenotypes. Each cli...

Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network.

PloS one
Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to...

Groundwater health probability risk prediction through oral intake using advanced optimization methods.

Journal of contaminant hydrology
Examining the cancer risk associated with oral groundwater (GW) intake is crucial, particularly in regions heavily reliant on GW for human consumption and agriculture. The study was based on real field investigations and controlled laboratory experim...