The main focus of this Research is to see how one can easily predict if he/she has been infected by COVID-19. Another aspect is deciding which COVID-19 symptom is more likely to show positive result of virus contamination. The virus has been declared as a pandemic and has affected more than 66,729,375 people across 220 countries and has also cost the lives of 1,535,982 people [source : who.int] as of the time this paper is being written. Research still predicts that another second wave is to hit soon. The required objective is obtained using complex machine learning algorithms that are able to predict, up to an extent the probability that the person has covid-19 and also if the related covid-19 symptoms provided are relevant to the condition or not. The algorithm used is a LOGIS TIC REGRES S ION algorithm that is used as a classification tool to separate the data into binary results, which in our case is if the person has covid-19 or not (YES OR NO). The dataset used to train this machine learning algorithm is obtained from online resources and a public survey.The machine learning model as of now has been able to predict the probability of virus contamination by 66.89% accuracy, further the model is able to relate if a given symptom is valid or not. With this we are able to conclude that the model is working fine and only fine tuning of the model is required in order to improve and enhance the accuracy and probability of exact results. The result were then improved using ANN( Artificial Neural Networks) but as it is computational expensive and due to lack of resources the expected performance cannot be met. The maximum accuracy achieved with ANNs was about 71%.

Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.

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Consideration of public health problem issues, one of the most common diseases in public is cancer. Most of the women population is suffering from breast cancer which is the most well known appearance of cancer in metropolitan cities of India and abroad. There many number of imaging modalities to diagnose cancerous cells. Among those, mammography is alone an imaging modality which diagnoses the breast cancer at an early stage. Furthermore, this modality involves X-rays which are more harmful to human health and make the patient inconvenience. Through the mammogram, doctors can analyze, estimate and evaluate the cancer stage so that doctors can give better and correct treatment to the patients. With this mortality and death rates can also be diminished up to some extent. In this paper, the author proposed an intelligent system to identify and find out the severity of breast cancer. By using a thermal based sensor which is of negative Temperature Coefficient (NTC) available with C-MET Thrissur which replaces Mammography. The stage at which the cancer is progressing is classified with the help of Intelligent System Algorithms which works on the temperature data obtained from the thermal device. The data is pre-processed and applied to multilayered backpropogation neural network model. The neural network classifies the preprocessed images into normal, benign and cancer. The output of the network is presented to the doctors through graphs and displays.

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