Prediction of Defective Products Using Logistic Regression Algorithm against Linear Regression Algorithm for Better Accuracy

Vasu, Vasantha Naga; Surendran, R; Saravanan, S.; Madhusundar., N

doi:10.1109/3ict56508.2022.9990653

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…An LR model is a type of ML model that is often used to predict the probability of an event as a function of a predictor variable, such as yes/no, true/false, and success/failure [31]. LR uses a mathematical function called the sigmoid function to estimate the probability that a dependent variable takes a particular value.…”

Section: Logistic Regressionmentioning

confidence: 99%

Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

Iparraguirre-Villanueva,

Espinola-Linares,

Flores Castañeda

et al. 2023

Diagnostics

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Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

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Section: Logistic Regressionmentioning

confidence: 99%