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.

Breast cancer is one the most critical disease and suffered many people around the world. The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease. In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data. In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer. Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection. The model performance evaluation metrics have been used for model performance evaluation. The BC data sets have been used for testing of the proposed method. The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms. The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy. We have been employed McNemar's statistical test for performance comparison of our different models. Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods. Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer. In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.

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.

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The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system.

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