A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned.  With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease

Explainable extreme boosting model for breast cancer diagnosis

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%

New hybrid decentralized evolutionary approach for DIMACS challenge graph coloring & wireless network instances

The Graph Coloring Problem is an NP-hard combinatorial optimization problem, and it is being used in different real-world environments. The chromatic integer is determined using different probabilistic methods. This paper explores a new hybrid decentralized evolutionary approach that applies the fixed colors and reduces the edge conflicts iteratively using greedy, split and conquer strategies. This article explores a new hybrid decentralized stochastic methodology for solving graph coloring. The method is constructed by combining the following strategies: Greedy heuristics, split & conquer, and decentralized strategy with an advanced & enhanced global search evolutionary operator. These hybrid design strategies are exhibited on complex DIMACS challenge benchmark graphs and wireless network instances. The proposed approach minimizes the complexity and converges to the optimal solution within a minimal time. The minimum percentage of successful runs obtained for the DIMACS benchmarks lies in (82%, 85%) except for the difficult instance latin_square_10.col, the vertices count n = 900 and edges count m = 307350. For the latin_square_10.col graph, the minimum color is reduced to 97 compared to other methods with less successful runs percentage. For the difficult instance flat1000_76_0.col graph, the minimum color is reduced to 76 compared to other methods, resulting in a better successful run. The method obtains the minimum color as χ(G) for the difficult instances le.col and flat.col graphs compared to other methods. The time taken to execute the developed technique is compared with the competing methods, and the proposed method outperforms very competitively in finding the minimum color for large graphs and also in finding the better solution with the high frequency of convergence (> 98%) in the channel allocation of wireless networks compared to the current methods

TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and Security

This book contains abstracts of the various research papers of the academic & research community presented at the International Conference on Innovations and Challenges in Computing, Analytics and Security (ICICCAS-2020). ICICCAS-2020 has served as a platform for researchers, professionals to meet and exchange ideas on computing, data analytics, and security. The conference has invited papers in seven main tracks of Data Science, Networking Technologies, Sequential, Parallel, Distributed and Cloud Computing, Advances in Software Engineering, Multimedia, Image Processing, and Embedded Systems, Security and Privacy, Special Track (IoT, Smart Technologies and Green Engineering). The Technical and Advisory Committee Members were from various countries that have rich Research and Academic experience. Conference Title: TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and SecurityConference Acronym: ICICCAS-2020Conference Date: 29-30 July 2020Conference Location: Pondicherry Engineering College, Puducherry – 605014, India (Virtual Mode)Conference Organizer: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.Conference Sponsor: TEQIP-III NPIU (A Unit of the Ministry of Human Resource Development, India)