Diagnosis of chronic disease in a predictive model using machine learning algorithm

Preethi, I.; Dharmarajan, K.

doi:10.1109/icstcee49637.2020.9276957

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…However, the procedure of converting worthless data to helpful is referred to as the data mining [6]. Data mining is a natural development of information technology, especially today with big data being used in discovery and analysis to establish logical relationships; data mining methods are being used in different areas, particularly in the medical field, as performing these methods helps clinicians select treatments, predict patient outcomes, and reduce costs in medicine, and instruct researchers to develop new treatments and refer patients to participate clinical trials [7]. Diagnosis in the medical field plays an important role in saving life, however, it is a complex task to be  ISSN: 2252-8938 Int J Artif Intell, Vol.…”

Section: Introductionmentioning

confidence: 99%

Comparison of various data mining methods for early diagnosis of human cardiology

Shanshool

Saeed²,

Khaleel³

2023

IJ-AI

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Recent healthcare reports indicate clearly an increasing mortality rates worldwide which puts a significant burden on the healthcare sector due to different diseases. Coronary artery diseases (CAD) is one of the main reasons of these uprising death rates since it affects the heart directly. For early diagnosis and treatment of CADs, a swiftly growing technology called data mining (DM) has been used to collect and categorize necessary data from patients; age, blood sugar and pressure, a type of thorax pain, cholesterol, and so on. Therefore, this paper adopted four DM methods; Decision tree (DT), logistic regression (LR), random forest (RF), and Naïve Bayes (NB) to achieve the goal. The paper utilized the Cleveland dataset along with Python programming language to compare among the four DM methods in terms of precision, accuracy, recall, and area under the curve. The results illustrated that NB method has the best accuracy of 89.47% compared with previous studies which will help with accurate, fast and inexpensive diagnosis of CADs.

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Section: Introductionmentioning

confidence: 99%

Comparison of various data mining methods for early diagnosis of human cardiology

Shanshool

Saeed²,

Khaleel³

2023

IJ-AI

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“…Machine learning contributes more in several domains. Many of the complex models make use of exiting larger training data, simultaneously at the edge of a major shift in healthcare epidemiology [9]. ese data can enhance the knowledge gain in the risk factors of diseases to reduce healthcare-associated infections, improve patient risk stratification, and find the way of transmitting the infectious diseases [10].…”

Section: Introductionmentioning

confidence: 99%

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Rayan

2022

Journal of Healthcare Engineering

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Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is difficult for doctors to manually identify the diseases accurately most of the time. The goal of this paper is to identify and predict the patients with more common chronic illnesses. This could be achieved by using a cutting-edge machine learning technique to ensure that this categorization reliably identifies persons with chronic diseases. The prediction of diseases is also a challenging task. Hence, data mining plays a critical role in disease prediction. The proposed system offers a broad disease prognosis based on patient’s symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person’s living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree, and logistic regression has been demonstrated in this paper.

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Chronic disease diagnosis and classification utilizing an intelligent classifier with WEKA tool

Tripathy¹,

Dash²,

Pattanayak³

2023

AIP Conference Proceedings

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Diagnosis of chronic disease in a predictive model using machine learning algorithm

Cited by 9 publications

References 18 publications

Comparison of various data mining methods for early diagnosis of human cardiology

Comparison of various data mining methods for early diagnosis of human cardiology

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Chronic disease diagnosis and classification utilizing an intelligent classifier with WEKA tool

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