Diseases is an unusual circumstance that affects single or more parts of a human’s body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those diseases, heart disease turns out to be the most common disease and the impact of this ailment is dangerous than all other diseases. In this paper, we compared a number of computational intelligence techniques for the prediction of coronary artery heart disease. Seven computational intelligence techniques named as Logistic Regression (LR), Support Vector Machine (SVM), Deep Neural Network (DNN), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbor (K-NN) were applied and a comparative study was drawn. The performance of each technique was evaluated using Statlog and Cleveland heart disease dataset which are retrieved from the UCI machine learning repository database with several evaluation techniques. From the study, it can be carried out that the highest accuracy of 98.15% obtained by deep neural network with sensitivity and precision 98.67% and 98.01% respectively. The outcomes of the study were compared with the outcomes of the state of the art focusing on heart disease prediction that outperforms the previous study.

Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients’ recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

Nowadays, Diabetes is one of the most common and severe diseases in Bangladesh as well as all over the world. It is not only harmful to the blood but also causes different kinds of diseases like blindness, renal disease, kidney problem, heart diseases etc. that causes a lot of death per year. So, it badly needs to develop a system that can effectively diagnose the diabetes patients using medical details. We propose a strategy for the diagnosis of diabetes using deep neural network by training its attributes in five-fold and tenfold crossvalidation fashion. The Pima Indian Diabetes (PID) data set is retrieved from the UCI machine learning repository database. The results on PID dataset demonstrate that deep learning approach design an auspicious system for the prediction of diabetes with prediction accuracy of 98.35%, F1 score of 98, and MCC of 97 for five-fold cross-validation. Additionally, accuracy of 97.11%, sensitivity of 96.25%, and specificity of 98.80% are obtained for tenfold cross-validation. The experimental results exhibit that the proposed system provides promising results in case of five-fold cross-validation.

Traveling Salesman Problem (TSP) is the most well-known combinatorial optimization real-world problem. TSP is also very popular to check proficiency in any newly developed optimization method. In addition, the optimization methods, which are developed for other tasks (e. g., numerical optimization), also test their proficiency in TSP. This study investigates a new technique to solve TSP based on a recently developed optimization technique stimulated through the foraging conduct of spider monkeys. Standard Spider Monkey Optimization (SMO) is established for numerical optimization which has six phases and each one has a different purpose. In this study, SMO is modified and updated to solve TSP; and Swap Operators (SOs), and Swap Sequence (SS) are considered to adapt SMO for TSP. In the proposed method, each spider monkey is considered as a TSP solution and SS is considered to update the solution. SS is an arrangement of several SOs in which each one holds two particular positions of a solution that might be swapped to make a new solution. All SOs of a SS is applied on a specific tour maintaining order and thus ramifications of the SS change the TSP tour into another one. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or randomly selected spider monkey) of the group. The proposed strategy has been examined on a huge number of benchmark TSPs and final consequences are compared to other prominent methods. SMO shows 11 best result out of 15 benchmark TSP problem compare to Ant Colony Optimization (ACO) and Velocity Tentative PSO (VTPSO). Experimental consequences show that the proposed strategy is a decent technique to solve TSP.