A Novel Approach for Classifying Diabetes’ Patients Based on Imputation and Machine Learning

Driss, Kaouthar; Boulila, ‪Wadii; Batool, Amreen; Ahmad, Jawad

doi:10.1109/ucet51115.2020.9205378

Cited by 20 publications

(7 citation statements)

References 17 publications

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“…e suggested technique has a low computing overhead and produces a secure ciphertext image within a few seconds. Our work can be further improved and modified to encrypt sensor data, biomedical data [33,34] in the future. Furthermore, the system can be improved using the concept of parallelism to encrypt massive amounts of multimedia data.…”

Section: Discussionmentioning

confidence: 99%

A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos‐Based Quantum Map

et al. 2022

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With the increasing volume of data transmission through insecure communication channels, big data security has become one of the important concerns in the cybersecurity domain. To address these concerns and keep data safe, a robust privacy-preserving cryptosystem is necessary. Such a solution relies on chaos encryption algorithms over standard cryptographic methods that possess multistage encryption levels, including high speed, high security, low compute overheads, and procedural power, among other characteristics. In this work, a secure image encryption scheme is proposed using linear feedback shift register (LFSR) and chaos-based quantum chaotic map. The focus of the scheme is mainly dependent on the secret keys from the input of the algorithm. The threat landscape, the statistical test analysis, along critical comparisons with other schemes indicate that the presented algorithm is significantly secure and is resistant to a wide range of different attacks such as differential and statistical attacks. The proposed method has sufficiently higher sensitivity and security when compared to existing encryption algorithms. Several security parameters validated the security of proposed work such as correlation coefficient analyses among the neighboring pixels, entropy, the number of pixels change rate (NPCR), unified average change intensity (UACI), mean square error (MSE), brute force, key sensitivity, and peak signal to noise ratio (PSNR) analyses. The randomness of the ciphers produced by the proposed technique is also passed through NIST-800-22. The results of NIST indicate that the ciphers are highly random and do not produce any type of periodicity or pattern.

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Section: Discussionmentioning

confidence: 99%

A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos‐Based Quantum Map

et al. 2022

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“…To fill in the blanks and lessen outliers, they used the imputation technique. K-NN was utilised to classify the dataset [8]. Using support vector machines, Pethunachiyar classified the Diabetes dataset from the UCI repository.…”

Section: Related Workmentioning

confidence: 99%

A critical review of machine learning approaches for predicting diabetes mellitus

Shobana¹,

Bushra²

2023

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Diabetes is a complex, chronic metabolic condition that progresses gradually and causes the patient to have increased blood glucose levels. The three most prevalent kinds of diabetes are type 1, type 2, and gestational. It results in serious health problems such renal problems, heart attacks, foot ulcers, and visual impairment. One of the primary symptoms is hyperglycemia. Early illness progression detection aids in the reduction of additional consequences. The classification of quantitative data most typically uses machine learning techniques. It makes it easier to pinpoint the important elements that are most responsible for the development of diseases. During the pre-processing stage, the least important properties are removed. The classification or forecasting of the condition Diabetes Mellitus using conventional. The discussion and analysis of supervised machine learning techniques focused on their predictive accuracy. The most popular and effective machine learning model among the several extant algorithms is examined using a variety of tools, datasets, and measures. One of the most effective machine learning models is the Deep learning algorithm of artificial neural networks, which is favored by many scientists and researchers in the field of health.

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“…In conclusion, the above study was shown to be both efficient and scalable. In what follows, Paul and Choubey (2017) in [5] proposed a new hybrid algorithm using a genetic algorithm (GA) for selecting the most suitable features in the PIDD dataset, and in [19] the Radial Basis Function Neural Network (RBFN) was used for classifying patient with diabetes and non-diabetes. The authors concluded that the hybrid method was better than the RBFN alone.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)

Faustin

Zou

2022

Computation

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The objective of the present study is to improve the genetic algorithm (GA) supremacy in selecting the most suitable and relevant features within a highly dimensional dataset. This results in cost reduction and improving classification performance. During text classification, employing terms such as features using vector space representation can result in a high dimensionality of future space. This condition presents some issues, including high computation cost in data analysis and deteriorating classification accuracy performance. Several computational feature selection techniques can be applied in eliminating the least significant features within a dataset, including a genetic algorithm. The present study improved the performance of the classifier in classifying Pima Indian diabetes data. Despite the popularity of GA in the feature selection area, it does not provide the most optimal features due to one of its underlying issues: premature convergence due to insufficient population diversity in the future generations. GA was improved in its crossover operator using two steps: define a variable slice point on the size of the gene to be interchanged for every offspring generation and apply feature frequency scores in deciding the interchanging of genes. The above obtained results to the proposed technique will be better results than the results for standard GA. Our proposed algorithm attained an accuracy of 97.5%, precision of 98, recall of 97% and F1-score of 97%.

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A Novel Approach for Classifying Diabetes’ Patients Based on Imputation and Machine Learning

Cited by 20 publications

References 17 publications

A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos‐Based Quantum Map

A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos‐Based Quantum Map

A critical review of machine learning approaches for predicting diabetes mellitus

An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)

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