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. 2022 Feb 25:2022:2826127.
doi: 10.1155/2022/2826127. eCollection 2022.

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Rayan Alanazi  1
Affiliations

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Rayan Alanazi. J Healthc Eng. .

Abstract

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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Conflict of interest statement

The author declares that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Architecture of proposed disease and risk prediction system.
Figure 2
Figure 2
Block diagram of convolutional neural network.
Figure 3
Figure 3
Calculation of Euclidean distance.
Figure 4
Figure 4
Comparison of accuracies of proposed and other algorithms.
Figure 5
Figure 5
Comparison of other performance evaluation metrics of proposed and other algorithms.
Algorithm 1
Algorithm 1
Convolutional neural network algorithm.
Algorithm 2
Algorithm 2
K-nearest neighbor algorithm.
See this image and copyright information in PMC

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