Abstract
Nowadays, diabetes disease is considered one of the key reasons of death among the people in the world. The availability of extensive medical information leads to the search for proper tools to support physicians to diagnose diabetes disease accurately. This research aimed at improving the diagnostic accuracy and reducing diagnostic miss-classification based on the extracted significant diabetes features. Feature selection is critical to the superiority of classifiers founded through knowledge discovery approaches, thereby solving the classification problems relating to diabetes patients. This study proposed an integration approach between the SVM technique and K-means clustering algorithms to diagnose diabetes disease. Experimental results achieved high accuracy for differentiating the hidden patterns of the Diabetic and Non-diabetic patients compared with the modern diagnosis methods in term of the performance measure. The T-test statistical method obtained significant improvement results based on K-SVM technique when tested on the UCI Pima Indian standard dataset.
Highlights
Diabetes disease is an incessant malady that happens when the pancreas does not create sufficient insulin or if the body cannot viably utilizes the insulin, it makes
The objective of this study is to develop a hybrid technique based on Support Vector Machine algorithm and Two-step clustering method for diabetes diagnosis
The proposed method seeks to reduce the ratio of misdiagnosis of diabetes and increase the ratio of accuracy for diagnosis
Summary
Diabetes disease is an incessant malady that happens when the pancreas does not create sufficient insulin or if the body cannot viably utilizes the insulin, it makes. Insulin is a hormone that controls blood sugar. Hyperglycemia, or raised blood sugar, is a typical impact of unrestrained diabetes and after sometimes prompts actual harm to vast numbers of the body's frameworks, the arteries and veins. Some studies that have been conducted recently presented common diseases that are frequently misdiagnosis therein; as the number of dead because of medical errors each year to nearly 98,000 people. The therapeutic analysis is viewed as an essential yet confused errand that should be executed precisely and proficiently The mechanization of this system would be to a significant degree beneficial. The objective of this study is to develop a hybrid technique based on Support Vector Machine algorithm and Two-step clustering method for diabetes diagnosis. The proposed method seeks to reduce the ratio of misdiagnosis of diabetes and increase the ratio of accuracy for diagnosis
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