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. 2024 Jul 3;24(1):1777.
doi: 10.1186/s12889-024-19261-8.

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data

Senobar Naderian  1   2 , Zeinab Nikniaz  3 , Mahdieh Abbasalizad Farhangi  4 , Leila Nikniaz  5 , Taha Sama-Soltani  6 , Parisa Rostami  2
Affiliations

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data

Senobar Naderian et al. BMC Public Health. .

Abstract

Background: Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually.

Objectives: This study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention.

Methods: The dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling.

Result: The study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study's emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia.

Conclusion: These results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.

Keywords: Data preprocessing; Dyslipidemia; Feature selection; Lifestyle promotion project; Machine learning; Multi-layer perceptron neural network; Predictive modeling; Random forest.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Unified methodology framework for effective classification and feature-outcome analysis
Fig. 2
Fig. 2
Novelty of the method
Fig. 3
Fig. 3
Main dyslipidemia factors: shared features across target variables
See this image and copyright information in PMC

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