Soft computing techniques for early diabetes prediction

Abstract

Diabetes mellitus is a chronic, life-threatening, and complicated condition. Around 1.5 million deaths due to diabetes have been documented, according to a World Health Organization (WHO) estimation in 2019. In the world of medicine, predicting diabetes risk is a difficult and time-consuming task. Many past studies have been conducted to investigate and clarify diabetes symptoms and variables. To solve these persisting issues, however, more critical clinical criteria must be considered. A comparative analysis based on three soft computing strategies for diabetes prediction has been carried out and achieved in this work. Among the computational intelligence methods used in this study are fuzzy analytical hierarchy processes (FAHP), support vector machine (SVM), and artificial neural networks (ANNs). The techniques reveal promising performance in predicting diabetes reliably and effectively in terms of several classification evaluation metrics, according to experimental analysis and assessment conducted on 520 participants using a publicly available dataset.

Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review

“…Saaty [23] was the first to introduce the AHP. As shown in Figure 4, AHP includes the decision's objective at the top, the criteria and subcriteria in the middle, and the collection of alternatives at the bottom [7]. The key benefits of AHP are its scalability and ease of usage.…”

“…General practitioners faced a significant problem when diagnosing diabetes, partly because patients displayed a wide range of signs and symptoms. This complex clinical environment confused general practitioners and changed the diagnostic procedure into a multiobjective health care decision-making challenge [7].…”

“…T2D is a significant global public health issue, characterized by 2 categories of harm: macrovascular (involving large arteries) and microvascular (involving small blood vessels). Macrovascular disease such as strokes and microvascular diseases such as retinopathy, nephropathy, and neuropathy [7]. MCDM techniques, especially TOPSIS, as shown in Table 5, are used to assist endocrinologists and general practitioners in analyzing the severity of these complications, forecasting their likelihood of occurrence, and pinpointing the risk factors for them (n=7).…”

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

“…The complications of type 2 diabetes, such as diabetic retinopathy, neuropathy, and cardiovascular disease, are a significant burden in India. S. A. Abdulkareem et al [ 16 ] conducted a comparative analysis of three soft computing techniques to predict diabetes risk: fuzzy analytical hierarchy processes (FAHP), support vector machine (SVM), and artificial neural networks (ANNs). The analysis involved 520 participants using a publicly available dataset, and the results show that these computational intelligence methods can reliably and effectively predict diabetes.…”

Prediction of linear model on stunting prevalence with machine learning approach

“…The huge impact of data growth makes data play a very important role in changing lives, from traditional to more modern and dynamic [11]. Artificial intelligence technology is very important to use to automatically detect and diagnose various types of diseases [12]. Building the model used in this study uses open data trained using machine learning to get the results of predictions and new information [13].…”