Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

The patient of Parkinson's disease (PD) is facing a critical neurological disorder issue. Efficient and early prediction of people having PD is a key issue to improve patient's quality of life. The diagnosis of PD specifically in its initial stages is extremely complex and time-consuming. Thus, the accurate and efficient diagnosis of PD has been a significant challenge for medical experts and practitioners. In order to tackle this issue and to accurately diagnosis the patient of PD, we proposed a machine-learning-based prediction system. In the development of the proposed system, the support vector machine (SVM) was used as a predictive model for the prediction of PD. The L1-norm SVM of features selection was used for appropriate and highly related features selection for accurate target classification of PD and healthy people. The L1-norm SVM produced a new subset of features from the PD dataset based on a feature weight value. For the validation of the proposed system, the K-fold cross-validation method was used. In addition, the metrics of performance measures, such as accuracy, sensitivity, specificity, precision, F1 score, and execution time, were computed for model performance evaluation. The PD dataset was in this paper. The optimal accuracy achieved the best subset of the selected features that might be due to various contributions of the PD features. The experimental findings of this paper suggest that the proposed method can be used to accurately predict the PD and can be easily incorporated in healthcare for diagnosis purpose. Currently, the computer-based assisted predictive system is playing an important role to assist in PD recognition. In addition, the proposed approach fills in a gap on feature selection and classification using voice recordings data by properly matching the experimental design.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient’s information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient’s information in the IoT enabled healthcare system.

Nowadays, mobile devices have become an integral part of our daily life. These have proven to be an advantageous scientific invention that fillspersonal and business needs in a very efficient manner. In this era, the availability of mobile services has significantly increasedbecause of the rich variety of mobile devices and essential applications provided by mobile device manufacturers. At the same time, numerous mobile security issues and data privacy threats are challenging both manufacturers and users. Therefore, mobile devices are an ideal target for various security issues and data privacy threats in a mobile ecosystem. In this paper, we provide a brief survey of the security challenges, threats, and vulnerabilities of amobile ecosystem. Furthermore, we discussed some key points required to ensure mobile security and defend against data privacy threats. The emphasis of the discussion is, strong protection and the restriction of malicious activity at theapplication developer end, application stores end, and operating system and mobile device manufactures end bypreventing the user from usingnon-recommended applications (which may be malicious) and consideringbiometric features for the authentication of real users in the mobile devices. Alsobriefly discussing the defense mechanismsthat are considered to bea relatively better approach for securing personal and business related data or information in the mobile devices

The effective detection of breast cancer is particularly essential for recovery and treatment in the initial phases. The existing methods are not successfully diagnosis breast cancer in the initial phases. Thus the initial recognition of breast cancer is expressively a great challenge for health professionals and scientists. To resolve the problem of initial stages recognition of breast cancer, we recommended a machine learning based diagnosis method which will excellently classify the malignant and benign persons. In the designing of our method machine learning model support vector machine has been applied to classify the malignant and benign persons. To increase the classification performances of the method, we used Minimal Redundancy Maximal Relevance and Chi-square algorithms to choose more appropriate features from the breast cancer dataset. The training/testing splitting technique is used for training and testing of the model. Additionally, the performance of the model has been evaluated by performance assessment metrics. The experimental results demonstrated that the classifier support vector machine obtained best classification performance on the selected subset of features as selected by Minimal Redundancy Maximal Relevance feature selection algorithm. The performances of support vector machine on selected features by Chi square feature selection algorithm are low as compared to Minimal Redundancy Maximal Relevance algorithm. From experimental results analysis, we determined that the integrated system based on Minimal Redundancy Maximal Relevance and support vector machine performances are high due to the selection of more suitable features and obtained 99.71% accuracy. According to McNemar’s statistical test the proposed method is more significant then existing methods. Thus, we recommend that the proposed diagnosis method for effective detection of breast cancer.