Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction

abdlhakim aouragh, Mohamed Bahaj, Fouad Toufik
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Abstract

Diabetes, a pervasive global health concern, presents diagnostic challenges due to its nuanced onset and far-reaching implications. Traditional diagnostic approaches, reliant on time-consuming assessments, necessitate a paradigm shift towards more efficient methodologies. In response, this study introduces a diagnostic support system leveraging the power of optimized machine learning algorithms. Addressing class imbalance within a dataset comprising 768 records, our methodology intricately weaves together feature selection, dimensionality reduction techniques, and grid search optimization. Specifically, the Extra Trees model, fine-tuned via grid search, emerges as the most potent, showcasing remarkable performance metrics: an accuracy score of 92.5%, an F1-score of 93.7%, and an AUC-ROC of 92.47%. These findings underscore the pivotal role of machine learning in reshaping diabetes diagnosis, offering transformative possibilities for global healthcare enhancement.
糖尿病预测:通过特征选择和降维优化机器学习
糖尿病是全球普遍关注的健康问题,由于其细微的发病原因和深远的影响,给诊断带来了挑战。传统的诊断方法依赖于耗时的评估,因此有必要转变模式,采用更高效的方法。为此,本研究利用优化机器学习算法的强大功能,引入了一种诊断支持系统。针对由 768 条记录组成的数据集中的类不平衡问题,我们的方法将特征选择、降维技术和网格搜索优化巧妙地结合在一起。具体来说,通过网格搜索进行微调的 Extra Trees 模型是最有效的模型,其性能指标非常出色:准确率为 92.5%,F1 分数为 93.7%,AUC-ROC 为 92.47%。这些发现强调了机器学习在重塑糖尿病诊断中的关键作用,为全球医疗保健的提升提供了变革性的可能性。
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