Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed.

The intricate and multifaceted nature of diabetes disrupts the body's crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew's correlation coefficient, receiver operating characteristic area, and precision-recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.

Investigating Students' Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach.

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques-Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors-are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs.

Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.

BACKGROUND: Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data. MATERIALS AND METHODS: In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned. RESULTS: Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy. CONCLUSIONS: The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.

A novel algorithm for thermal image encryption.

Thermal images play a vital character at nuclear plants, Power stations, Forensic labs biological research, and petroleum products extraction. Safety of thermal images is very important. Image data has some unique features such as intensity, contrast, homogeneity, entropy and correlation among pixels that is why somehow image encryption is trickier as compare to other encryptions. With conventional image encryption schemes it is normally hard to handle these features. Therefore, cryptographers have paid attention to some attractive properties of the chaotic maps such as randomness and sensitivity to build up novel cryptosystems. That is why, recently proposed image encryption techniques progressively more depends on the application of chaotic maps. This paper proposed an image encryption algorithm based on Chebyshev chaotic map and S8 Symmetric group of permutation based substitution boxes. Primarily, parameters of chaotic Chebyshev map are chosen as a secret key to mystify the primary image. Then, the plaintext image is encrypted by the method generated from the substitution boxes and Chebyshev map. By this process, we can get a cipher text image that is perfectly twisted and dispersed. The outcomes of renowned experiments, key sensitivity tests and statistical analysis confirm that the proposed algorithm offers a safe and efficient approach for real-time image encryption.