# Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
## 3 AI Applications  
### Data Mining  
Data mining is the process of extraction of knowledge and features from data using automatic or semiautomatic methods, it combines machine learning and data science. Indeed, data mining describes, discovers relevant patterns, associations, and knowledge to make predictions, according to pre-established criteria. In healthcare, data mining is becoming increasingly popular and essential. There is large potential for use of data mining in healthcare. These can be grouped as the management of healthcare, evaluation of diagnosis and treatment effectiveness; customer relationship management; detection of fraud and abuse. Applications of data mining techniques in breast cancer diagnosis, treatment, and prognosis was the subject of a study provided in [3]. Generally, two phases for making the decisions are distinguished in the medical world: differential diagnosis (DD) and final or provisional diagnosis (FD). In the first phase the medical history of the patients, the symptoms of disease, the results of different laboratory tests (blood testing, etc.) are perceived by physicians as the input data. The latter are handled by physicians based on their medical knowledge for disease diagnosis. In the second stage and given the identified disease, first recommendations and treatments would start. Until now, one of the important challenges in cancer treatment is to determine the most common symptoms that can help in cancer diagnosis. There were many research studies conducted to identify the patterns of cancers and create a smart method for diagnosis of tumors in the early stages. The method also suggests the best treatments. To identify kinds of cancers (benign breast tumors from malignant ones), most of physicians undertake surgical biopsy. However, most of them believed that biopsy is a critical task and should be prevented when possible. Consequently, proposing a smart system that can help physicians to determine the type of cancer and avoid surgical biopsy would be helpful for patients as well as for physicians. In [3], the authors attempted to cover research works related to diagnosis of breast cancers, when applying various techniques of data mining along with their results. Most of the reviewed research works (on diagnosis, treatment, or prognosis of breast cancers) are concerned with comparing the accuracy rate of data mining various algorithms or techniques. There is not yet a tool to automatically diagnose or predict breast cancer. Further, there is no research work that applies customized features to offer the best treatments to patients. In [4] [PERSON] et al. reviewed and analyzed the current medical data classification practices, advancements, and prospects using data mining. They highlighted the main approaches used to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**AI Applications** & **Found** & **Relevant** & **Cited** \\ \hline Data mining & 5987 & 1052 & 29 \\ Ontologies and semantic reasoning and clinical guidelines & 2912 & 675 & 34 \\ Clinical decision support systems & 3970 & 2198 & 21 \\ Smart homes & 6444 & 2932 & 30 \\ Medical big data & 5469 & 689 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Systematic review stages. enhance classification accuracy. There is a wide literature which suggests that data mining is very effective for the classification task. Among the algorithms of data mining used in healthcare applications are the following: decision trees [5; 6; 7; 8; 9], neural networks [10; 11; 12; 13; 14; 15], SVM [16; 17; 18; 19], k-means [20; 21; 22], logical regression [23], KNN (K-nearest neighbors) [24], and naive Bayes classifier [25; 26; 27]. These algorithms can be combined to improve the data analysis process as it is done in [28] where the authors combined random forest, decision trees, neural network, SVM, logistic regression, and KNN algorithms to find out the health and anticipate the mortality risks of patients contaminated with COVID-19.