Multiple disease prediction using Machine learning algorithms

被引：43

作者：

Arumugam K. ^{[1
]}

Naved M. ^{[2
]}

Shinde P.P. ^{[3
]}

Leiva-Chauca O. ^{[4
]}

Huaman-Osorio A. ^{[5
]}

Gonzales-Yanac T. ^{[4
]}

机构：

[1] Department of Computer Science, Karpagam Academy of Higher Education, Tamilnadu, Coimbatore

[2] Department of Business Analytics, Jagannath University, Delhi-NCR

[3] Master of Computer Application Department, Government College of Engineering, Maharashtra, Karad

[4] Administration and Tourism Faculty, Universidad Nacional Santiago Antúnez de Mayolo, Huaraz

[5] Economics and Accounting Faculty, Universidad Nacional Santiago Antúnez de Mayolo, Huaraz

来源：

Materials Today: Proceedings | 2023年 / 80卷

关键词：

Accuracy; Classification; Data mining; Decision tree; Machine learning; Naïve bayes; Prediction; Support vector machine;

D O I：

10.1016/j.matpr.2021.07.361

中图分类号：

学科分类号：

摘要：

Data mining for healthcare is an interdisciplinary field of study that originated in database statistics and is useful in examining the effectiveness of medical therapies. Machine learning and data visualization Diabetes-related heart disease is a kind of heart disease that affects diabetics. Diabetes is a chronic condition that occurs when the pancreas fails to produce enough insulin or when the body fails to properly use the insulin that is produced. Heart disease, often known as cardiovascular disease, refers to a set of conditions that affect the heart or blood vessels. Despite the fact that various data mining classification algorithms exist for predicting heart disease, there is inadequate data for predicting heart disease in a diabetic individual. Because the decision tree model consistently beat the naive Bayes and support vector machine models, we fine-tuned it for best performance in forecasting the likelihood of heart disease in diabetes individuals. © 2021

引用

页码：3682 / 3685

页数：3

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