How AI can help in Cancer Prediction-Humaci.com

1. How AI can help in Cancer Prediction- Humaci.com Cancer diagnosis in the early stages of the disease in patients can make a significant impact on both survivabilities as well as the level of invasive procedures necessary. It is, therefore, essential to be able to predict whether or not a patient has cancer correctly. Artificial intelligence, machine learning, and deep learning algorithms are so widespread in terms of application that they can also be used as tools to analyze data from various tests and diagnoses of patients and then predict an accurate result of whether or not the patient has cancer. Machine learning is divided into two categories supervised and unsupervised learning. Supervised learning deals with the analysis of labelled data, whereas unsupervised learning deals with unlabelled data. Since the data that is taken up to train a predictive model of a cancer diagnosis is labelled, predicting cancer would fall under this category. Further, supervised learning is divided into regression and classification. Regression results in a continuous-valued output, while classification results of the binary production. Since predicting cancer can have only two logical solutions, i.e. a patient has cancer or a patient does not have cancer. Therefore the cancer prediction is a classification problem under supervised learning. There are various classifier algorithms and models that can be taken into consideration for predicting cancer. One of these is logistic regression. Logistic regression builds a hypothesis using all the features, and then that hypothesis is applied on a sigmoid function to give a binary output of malignant tumour or benign tumour. This hypothesis is a product of a vector of parameters and the vector of independent variable x. While choosing the parameters, we calculate its cost function and then try to tune our parameters to an optimum value. The idea behind the optimum values of parameters is that they should result in a minimum valued curve of the cost function. In other words, the cost function should guarantee a convex curve. Another method of diagnosing whether a person has cancer or not is by using a support vector machine classifier. A support vector machine is a classification of the decision boundary of which minimizes error. An SVM classifies by creating a hyperplane between two classes of data points. This divides the output into malignant or benign. It is useful for higher dimensional spaces. It also uses memory very efficiently as it uses a subset of training points called support vectors. But if the number of features is much higher than the number of samples, then overfitting is very likely. A Bayesian Networks Classifier can also be used to predict cancer in patients. It is similar to decision trees, but it depends more on mathematical outputs rather than predictive ones. This means that it shows probabilistic estimations rather than predictions. An Artificial Neural Network can also be used to predict cancer where much data is fed, and it automatically

2. identifies data features and labels it. Then in various layers, i.e. input layer, hidden layers, and output layer, it assigns weight and then bias using activation functions which by the method of linear combination gives a predictive value. If the value is higher than the threshold, then cancer is present else it is not present. A computational model such as machine learning or deep learning models has been known to give an accuracy of more than eighty per cent while predicting breast cancer in patients. It is highly efficient and time taken in its analysis is very less compared to human review. Using it, rather than having only a single model or method of prediction manually, machine learning and deep learning algorithms can predict individual cancer patient solutions based on their responses to medicine as well. Artificial intelligence can predict how likely a person is to get cancer, i.e. their vulnerability to cancer. This can be done by using an artificial neural network. Artificial intelligence can predict the reappearance of cancer as well. This can be achieved using Bayesian Classifiers, artificial neural networks, support vector machines, decision trees, and random forest classifiers. Once diagnosed with cancer, various models built using AI can predict the survival rates of a patient. This can be done by basing a dependence on features that have changed significantly with medical procedures and therapy. Shortly, artificial intelligence will revolutionize the medical industry. It is currently being used so extensively in the fields of medicine, yet some areas need to be worked. One of these areas, in terms of cancer diagnosis, analysis, and cure, is the increase in terms of accuracy. Even though the accuracy in diagnosing cancer is high, the accuracy of prediction the rate of development of cancer is still very nominal. While the accuracy is always correlated to the efficiency of a predictive model, it is also important to note that these machine learning models are built on the data provided to them. With time, the methods of extracting all the characteristic features attributing to cancer have become more sophisticated. Therefore, a direct influence of this is on the accuracy of models for predicting cancer. In the near future, AI is set to automate the cancer assessment phase fully. This would include giving a detailed analysis of every single feature contributing to cancer in a patient and to what percentage their contribution is. This could help in tackling cancer better and would result in a deeper understanding and better solution against it.