IPL: From Lens of Data Science

Pradhan, Rahul; Maheshwari, Drashti; Chaturvedi, Ankur; Sharma, Dilip Kumar

doi:10.1007/978-3-030-76736-5_34

Cited by 2 publications

(1 citation statement)

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“…In Reference (10), authors conducted a comparative evaluation of various classifiers on the diabetes dataset collected from Kaggle. In this work, LR, KNN (K-nearest neighbor), SVM, Decision Tree (DT), Naïve Bayes (Gaussian, Multinominal, Bernoulli), and AdaBoost classifier used.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Three Predictive Algorithms for Diabetes Mellitus Against the Pima Indians Dataset

Darolia¹,

Chhillar²

2022

ECS Trans.

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Diabetes mellitus is a metabolic disease in which the pancreas fails to produce enough insulin required for the processing of blood glucose. Most medical institutions analyze Electronic Health Records (EHRs) manually and then predict whether the patient is diabetic or not. The objective of this work is to classify diabetes and non-diabetes patients using predictive algorithms/techniques. These algorithms provide cost, time, and effort-effective solutions for the prognosis and diagnosis of diabetes mellitus. In this work, popular algorithms like Artificial Neural Network (ANN), Random Forest (RF), and Logistic Regression (LR) have been used against the PIMA Indians. Dataset and analysis has been carried out on open-source software WEKA. In addition, this paper provided state-of-the-art by various researchers related to the said topic. This work concluded that LR outperforms other algorithms, with the accuracy of 77.10%, but in the case of area under the curve (0.83), both LR and RF perform equally well.

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Section: Related Workmentioning

confidence: 99%