Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94% respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient's real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the How to cite this paper:

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Herbal medicines have traditionally been used worldwide for the prevention and treatment of liver disease with fewer adverse effects. The leaves of the Syzygium jambos (SJL) plant were chosen and studied for their antioxidant activity in vitro and hepatoprotective activity in vivo. The antioxidant activity of the ethanol extract was examined in vitro using a 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging assay, reducing capacity, total phenol, total flavonoid content, and total antioxidant capacity. The extract had significant dose-dependent antioxidant activity in all in vitro experiments. IC 50 values of SJL and ascorbic acid (standard) were found to be 14.10 and 4.87 μg/mL, respectively, according to a DPPH radical scavenging assay. Hepatoprotective activity of the plant extract was evaluated in a rat model of carbon tetrachloride (CCl 4 )-induced liver damage. CCl 4 significantly altered serum marker enzymes, total bilirubin, total protein, and liver weight. The extract caused these values to return to normal in rats with CCl 4 -induced liver damage that were given SJL. This indicated the hepatoprotective potential of SJL and was comparable to use of the standard drug silymarin. Thus, the present study revealed that SJL may have antioxidant and hepatoprotective activity.

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