International Journal of Preventive Medicine and Health
EISSN : 2582-7588
Published by: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP (10.35940)
Total articles ≅ 5
Articles in this journal
Published: 10 September 2021
International Journal of Preventive Medicine and Health, Volume 1, pp 1-5; https://doi.org/10.35940/ijpmh.b1005.091421
Robotic and advanced technology rehabilitation is useful for people with difficulties and deficits in arm and hand movements, walking problems and balance disorders. Robotic technologies are being introduced in the rehabilitation field to support the activity of specialists, doctors and physiotherapists; the future and the challenge of rehabilitation lies precisely in the development of robotics. Robot assists the therapist in administering the most appropriate motor therapy with precision and repeatability modulates the difficulty of the exercise. It allows repetitive task-oriented activities with augmentative feedback capable of inducing brain plasticity. It acquires quantitative information on movement and evaluates the services performed he first, “Arm and Hand”, is used to help the opening and closing movements of the hand. After entering it by hand and forearm, gently guides the patient's shoulder and elbow movements to reach and grasp objects. “Wrist”, on the other hand, interacts with the movements of the wrist and integrates functionally with the “Hand” module.
Published: 10 July 2021
International Journal of Preventive Medicine and Health, Volume 1, pp 1-4; https://doi.org/10.35940/ijpmh.c1010.071321
Kidney diseases are increasing day by day among people. It is becoming a major health issue around the world. Not maintaining proper food habits and drinking less amount of water are one of the major reasons that contribute this condition. With this, it has become necessary to build up a system to foresee Chronic Kidney Diseases precisely. Here, we have proposed an approach for real time kidney disease prediction. Our aim is to find the best and efficient machine learning (ML) application that can effectively recognize and predict the condition of chronic kidney disease. We have used the data from UCI machine learning repository. In this work, five important machine learning classification techniques were considered for predicting chronic kidney disease which are KNN, Logistic Regression, Random Forest Classifier, SVM and Decision Tree Classifier. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated by test dataset. The analysis results show that Decision Tree Classifier and Logistic Regression algorithms achieved highest performance than the other classifiers, obtaining the accuracy of 98.75% followed by random Forest, which stands at 97.5%.
Published: 10 May 2021
International Journal of Preventive Medicine and Health, Volume 1, pp 1-4; https://doi.org/10.35940/ijpmh.b1002.051221
Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.
Published: 10 May 2021
International Journal of Preventive Medicine and Health, Volume 1, pp 5-7; https://doi.org/10.35940/ijpmh.b1003.051221
Healthcare Informatics plays a very important role for manipulating data. In the healthcare discoveries, pattern recognition is important for the prediction of depression, aggression, pain and severe disease diagnostics. In , the real innovation that has affected and organized human services is cloud computing, which empowers whenever anyplace access to the information put away in a cloud. The mobile devices are continuously observing patients that move around a networked healthcare environment. In traditional healthcare diagnostic system, we depend upon expensive tests and machineries which increase the expenditure of healthcare. It is dire need to reduce the aggregate cost of regular or usual diagnostics incorporates high cost of hospitalization. These expenses can be limited or disposed of with the assistance of remote patient monitoring gadget, a healthcare IoT product. Remote monitoring of person’s health gadget includes the observing of a person from an alternate area. This dispenses the requirement for driving to clinic and to being hospitalized for less severe circumstances. This research will explore the depression monitoring system by detecting the facial expression using suitable soft computing algorithm. We may use different algorithms such as CNN and Multilayer Perceptron to get the best result. On the basis of classification it detects the class of disease. For this purpose, the primary dataset from various facial expressions of a patient will be collected, filtered and apply to classification algorithm to train the model.
Published: 10 November 2020
International Journal of Preventive Medicine and Health, Volume 1, pp 1-7; https://doi.org/10.35940/ijpmh.a2005.111120
Covid-19 pandemic has changed the routines of families all over the world. From March 2020 up to today, Italian families are still struggling for adaptation. Parents of children and adolescents with a clinical diagnosis are more at risk for parental burnout, depression, and anxiety, and they are now experiencing restrictions in many services families relied on. Home-based and hospital-based interventions based on the Play Specialist’s approach have been limited due to anti-covid norms. Internationally, Play Specialist intervention has been empirically demonstrated effective in diminishing children’s negative emotions in relation to medical procedures and in increasing adaptation and compliance towards medical settings. Plus, Play Specialist’s intervention indirect effect on parental wellbeing is still unexplored. In Italy, differently from UK and USA, the Play Specialist intervention is not certified in the health-care system yet. The present study tests the effects on parental psychosocial health of a telematic adaptation of the Play Specialist approach (TPS), conducted in the post-lockdown months in Italy. Two groups of parents (N=33, Mean age=43.36, SD=9.81, Female= 66% receiving the TPS intervention, and N=33 Mean age=41.84, SD=6.15, Female=78% controls) of children in clinical conditions are compared. Parental burnout, anxiety, stress, depression, social support, and parental perception of children’s emotional problems have been measured via self-report questionnaires. Analysis of covariance reveals that the TPS group is less stressed, perceives higher social support, lower parental burnout (i.e., emotional distancing, contrast with other/previous Self, fed-up feeling), lower emotional and behavioural child’s problems than the control group. These findings are addressed at encouraging both research and practice around the Play Specialist’s intervention beyond the hospital-context.