A new approach based on machine learning to certain diseases

Never in history is the importance of data science so emphasized in modern society. Focusing on obtaining conclusive results from the implicit features concealed in a huge amount of data, data science plays a remarkable role in various fields, including modern medical practice. Although the fascinating performance of cutting-edge technology is capable of coping with numerous diseases, certain diseases, such as breast cancer and Parkinsons disease, still compromise peoples health since these diseases are difficult to predict and prone to exacerbate. In order to deal with that problem, we will introduce three different machine learning methods in our experiment to two different data sets to test the performance of classification. In the paper, we clarified the principle of each machine learning method (three different classifiers) at first. Then, we conducted our experiment, during which decisive parameters of classifiers were set by specific searching algorithms. Besides, we introduced metrics along with their principles for the evaluation of the numerical results, which were obtained by different classifiers. In the next step, we discussed the results by comparing the values of the metrics that represent the performance of a particular method. Therefore, we managed to obtain optimal classifiers for the two datasets. In the final stage of the paper, we discussed our experiments limitations as well as prospects, which includes further application in other fields.