Prediction and classification of diabetes mellitus using genomic data

Awotunde, Joseph Bamidele; Ayo, Femi Emmanuel; Jimoh, Rasheed Gbenga; Ogundokun, Roseline Oluwaseun; Matiluko, Opeyemi Emmanuel; Oladipo, Idowu Dauda; AbdulRaheem, Muyideen

doi:10.1016/b978-0-12-821187-8.00009-5

Cited by 7 publications

(2 citation statements)

References 213 publications

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“…3 Random over sampling to reduce class imbalance problem 2. 4 Z-score normalization for rescaling numeric columns in a dataset…”

Section: 2mentioning

confidence: 99%

“…High blood glucose, often known as hyperglycemia, is caused by inadequate insulin production and activity in the human body. Diabetes causes a variety of diseases, including retinopathy, heart attack and stroke, nephropathy, neuropathy, skin diseases (such as bacterial and fungal infections), hearing problems [4,5], vision loss, kidney failure or renal disease, congestive heart failure, stroke, and lower limb amputation. According to the WHO, over 4.2 million people worldwide are expected to die from diabetes and its complications in 2019, with diabetes accounting for 2% of all fatalities in India.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Approach for Detection of Type-2 Diabetes in Women Using Lasso Regression and Artificial Neural Network

Singh¹,

Tiwari²

2022

IJISA

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Diabetes is a life-threatening and long-lasting illness that produces high blood glucose levels. Diabetes may cause various diseases, including liver disease, blindness, amputation, urinary organ infections, etc. This research work aims to introduce a hybrid framework to enhance outcomes predictability and interoperability with reduced ill-posed problems, over-fitting problems, and class imbalance problems for diagnosing diabetes mellitus using data mining techniques. Diabetes may be recognized in many ways. One of these methods is data mining techniques. The use of data mining to medical data has yielded meaningful, significant, and effective results that may improve medical expertise and decision-making. This study suggests a hybrid technique for detecting DM that combines the lasso regression algorithm with the artificial neural network (ANN) classifier algorithm. The Lasso regression technique is used for variable selection and regularization. Because the dataset was shrunk, the computing time was considerably minimized. The ANN classifier received the Lasso regression output as an input and classified patients correctly as diabetic and non-diabetic, i.e., tested positives and negatives. The Pima Indians dataset was used in this experiment, consisting of 768 samples of female participants who are diabetic and non-diabetic. According to experimental observations, the proposed hybrid technique achieved 93% classification accuracy for predicting diabetes mellitus. The experimental results showed that our proposed method had a classification accuracy of 93% for determining whether a patient has diabetes or not. The experimental outcomes demonstrated that a hybrid data-mining approach might assist clinicians in making better diagnoses when identifying diabetes patients.

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“…3 Random over sampling to reduce class imbalance problem 2. 4 Z-score normalization for rescaling numeric columns in a dataset…”

Section: 2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%