Computer Science, Artificial Intelligence, Management of Technology and Innovation, Analysis
2
Scopus Publications
5
Scholar Citations
1
Scholar h-index
Scopus Publications
Heart diseases prediction based on multiple machine learning models Explainable Artificial Intelligence in Healthcare Systems, 2024
Forecast of Health Risk for Chronic Kidney Disease A Comparison between Naïve Bayes (NB) and Support Vector Machine (SVM) Models Suman Halder, Sree Kumar, Debi Prassana Acharjya, Sambhu Dutta Computer Vision and AI Integrated Iot Technologies in the Medical Ecosystem, 2024 Human kidneys are well-adapted to excrete the daily acid load from diet and metabolism in order to maintain homeostasis. There are many life-threating diseases among them kidney disease are fierce and major damage to humankind. One of them being chronic kidney disease (CKD). CKD is a non-communicable disease that includes a range of different physiological disorders that are associated with an abnormal renal function and progressive decline in glomerular filtration rate (GFR). Some CKDs cannot be cured entirely, though they can be prevented. CKD is a global health burden with a high economic cost to health systems and is an independent risk factor for cardiovascular disease (CVD). All stages of CKD are associated with increased risks of cardiovascular morbidity, premature mortality, and/or decreased quality of life. The objective of this chapter is to predict whether the patient can have CKD based on the various input variables, viz. age (age), blood pressure (bp), sugar (su), red blood cells (rbc), pus cell (pc), hypertension (htn), diabetes mellitus (dm), etc. The output variable of the study is termed “Classification,” which is a binary variable taking values 1 or 0. Value 1 stands for the presence of CKD, and value 0 stands for the absence of CKD. Here, results of classification algorithms viz. the Naïve Bayes (NB) model and a support vector machine (SVM) are used to detect CKD early. Herein, the NB method categorization technique is used to predict a member of each class considering the target class's probability for the given data, whereas SVM offers good accuracy compared to other algorithms in health data analytics. SVMs are easily interpretable with an efficient classification, which enhances the predictive accuracy for health problems. The models are evaluated based on the accuracy score metric to find the best model. The data used for the study is taken from Kaggle.
RECENT SCHOLAR PUBLICATIONS
Forecast of health risk for chronic kidney disease: a comparison between naïve bayes (NB) and support vector machine (SVM) models S Halder, S Kumar, DP Acharjya, S Dutta Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem … , 2024 2024.0 Citations: 5
Chronic Kidney disease Prediction- A Comparison between SVM and Logistic Regression H Suman, D Sreekumar Application of Statistics and Artificial Intelligence in Emerging Scenario … , 2023 2023.0
CHRONIC KIDNEY DISEASE (CKD) PREDICTION–A COMPARISON BETWEEN S Halder APPLICATIONS OF STATISTICS & ARTIFICIAL INTELLIGENCE IN EMERGING SCENARIOS … , 0
MOST CITED SCHOLAR PUBLICATIONS
Forecast of health risk for chronic kidney disease: a comparison between naïve bayes (NB) and support vector machine (SVM) models S Halder, S Kumar, DP Acharjya, S Dutta Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem … , 2024 2024.0 Citations: 5
Chronic Kidney disease Prediction- A Comparison between SVM and Logistic Regression H Suman, D Sreekumar Application of Statistics and Artificial Intelligence in Emerging Scenario … , 2023 2023.0
CHRONIC KIDNEY DISEASE (CKD) PREDICTION–A COMPARISON BETWEEN S Halder APPLICATIONS OF STATISTICS & ARTIFICIAL INTELLIGENCE IN EMERGING SCENARIOS … , 0
