Inicio  /  Applied Sciences  /  Vol: 14 Par: 5 (2024)  /  Artículo
ARTÍCULO
TITULO

Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions

Rejath Jose    
Faiz Syed    
Anvin Thomas and Milan Toma    

Resumen

The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library?a Python-based machine learning toolkit?to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics.

 Artículos similares

       
 
Md. Mohibbullah, So-Jung Park, Jae-Suk Choi and Sae-Kwang Ku    
Obesity is implicated as a factor in several serious metabolic conditions, including hypertension, cardiovascular disease, and type II diabetes. This study aimed at the development of more potent and safer alternative medications to address these metabol... ver más
Revista: Applied Sciences

 
Jin-A Lee and Keun-Chang Kwak    
Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Ph... ver más
Revista: Applied Sciences

 
Paraskevi Detopoulou, Panos Papandreou, Lida Papadopoulou and Maria Skouroliakou    
Clinical Decision Support Systems (CDSSs) facilitate evidence-based clinical decision making for health professionals. Few studies have applied such systems enabling distance monitoring in the COVID-19 epidemic, especially in a hospital setting. The purp... ver más
Revista: Applied Sciences

 
Xuebin Xu, Chen Chen, Kan Meng, Longbin Lu, Xiaorui Cheng and Haichao Fan    
Sleep, as the basis for regular body functioning, can affect human health. Poor sleep conditions can lead to various physical ailments, such as poor immunity, memory loss, slow cognitive development, and cardiovascular diseases. Along the increasing stre... ver más
Revista: Applied Sciences

 
Nafisa Anjum, Khaleda Akhter Sathi, Md. Azad Hossain and M. Ali Akber Dewan    
By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and providing information about the patient?s cardiac health to the special... ver más
Revista: Computers