Effective Classification of Chronic Kidney Disease Using Extreme Gradient Boosting Algorithm

Effective Classification of Chronic Kidney Disease Using Extreme Gradient Boosting Algorithm

Ramya Asalatha Busi, M. James Stephen
Copyright: © 2023 |Pages: 18
DOI: 10.4018/IJSI.315732
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With a high rate of morbidity and mortality, chronic kidney disease is a global health issue that also causes other diseases. Patients frequently overlook the condition because there aren't any evident symptoms in the early stages of CKD. An efficient and effective Extreme gradient boosting method for the early diagnosis of kidney illness has been proposed in this paper to explore the capability of various machine learning algorithms. DenseNet can extract a variety of features such as vector features. After that feature extraction phase, the data are fed into the feature selection phase. The features are selected based upon the Improved Salp swarm Algorithm (ISSA). The proposed CKD classification method has been simulated in PYTHON. Utilizing the CKD dataset from the UCI machine learning resources, the dataset is then tested. Sensitivity, accuracy, and specificity are the performance metrics used for the proposed CKD classification approach. The results of the experiments demonstrate that the proposed approach outperforms the present state-of-the-art method in classifying CKD.
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A serious death and illness problem is enforced by CKD, often known as CKD (Ammirati, 2020). It is one of the non-communicable diseases with one of the fastest expanding epidemiologies. CKD is a condition where the kidneys lose their ability to filter blood, allowing the body's waste products to build up within and leading to other health issues (Henry & Lippi, 2020; Jankowski et al., 2021; Byrne & Targher, 2020). Because clean, pure blood aids in the improved functioning of the body's organs, it is extremely vital to maintain healthy kidney function. Over many years, this harm develops (Portolés et al., 2020). Kidney function decreases as damage increases, which is bad for the body. In developing and underdeveloped nations, it is increasingly becoming a serious hazard. Diseases like diabetes and high blood pressure are the main causes of its onset (FIDELIO-DKD Investigators et al., 2021; Guzzi et al., 2019; Bidin et al., 2019). In addition to obesity, heart disease and a family history of CKD, other risk factors contribute to CKD.

Testing may be the only way to determine whether the patient has the renal disease because, in its initial stages, CKD has no manifestations (Connaughton et al., 2019; Paik et al., 2022). Early identification of CKD in its initial phases can enable the patient to receive appropriate treatment and halt the development of ESRD (Zhuang et al., 2021). It is suggested that everyone with a risk factor for CKD, such as a family history of renal failure, high blood pressure, or diabetes, should be examined annually (Mihai et al., 2018). This illness is characterized by a gradual decline in renal function, which leads to a full loss of renal function in the end.

Early on, CKD does not manifest any overt symptoms. As a result, the disease might not be identified until the kidney has lost about 25% of its functionality (Kumar et al., 2022). Additionally, CKD affects the human body globally and has a high rate of morbidity and mortality. Cardiovascular disease may develop as a result (Han et al., 2020). A pathologic illness that progresses and cannot be reversed is CKD. Therefore, early detection and diagnosis of CKD are crucial for allowing patients to begin treatment and halt the disease's progression (Chen et al., 2019).

Diabetes, high blood pressure, and cardiovascular disease (CVD) are risk factors for CKD patients. Patients with CKD experience side effects, particularly in the late stages, which weaken the immunological and nervous systems (Sharma, 2018; Siraj, 2019). Patients may be in advanced stages in developing nations, necessitating dialysis or kidney transplants. Glomerular filtration rate (GFR), a measure of kidney function, is used by medical professionals to identify renal illness. Age, blood test results, gender, and other patient-related characteristics are taken into account while calculating GFR. Doctors can divide CKD into five stages based on the GFR value (Wang et al., 2019).

Machine learning describes a computer program that evaluates and extrapolates task-related data to determine the traits of the associated pattern (Gunasundari et al., 2018). This technology is capable of making cost-effective and accurate diagnoses of diseases, making it a potentially useful tool for CKD diagnosis (Calderon-Margalit et al., 2018). With the advancement of information technology, it has evolved into a new kind of medical instrument and has a wide range of potential applications.

The following are the contributions of the proposed research:

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