# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 2 Literature Review  
The literature has been reviewed in two broad areas. At first, various articles are reviewed explaining state-of-the-art decision forest algorithms proposed in the field of data science. Second, few promising articles related to decision forests algorithms used for [PERSON]'s disease detection has been discussed. Decision forests have been proposed by many researchers and widely used due to the ability to achieve maximum predictability of incoming instances. [PERSON] [26] introduced the first version of Random Forest as a decision forest algorithm. Random Forests undertakes bagging as the training method on Random Subspace. For a set of features \(f\) the size of features chosen randomly for training purposes are \(int(log_{2}|m|)+1\), where m is the number of inputs [25]. The benefit of Random Forest is its versatility to varying data size. However, the major drawback of Random Forest lies with the number of trees that it employs for classifying data. However, Random Forest employs a significant number of trees for effective training and classification of incoming instances. At the same time, with the increase in number of trees the time complexity of Random Forest is also increases [33]. There are many variations of Random Forest [34; 35; 36; 37; 38; 39] are proposed so far. [PERSON] & [PERSON] [40] proposed a perfect random tree ensemble, where the attribute behind the splitting criteria is chosen randomly. Cascading and Sharing Trees, a decision forest algorithm ranks the features according to their contribution towards the classification. The gain ratio scores are allocated to the attributes for ranking. At the post ranking phase, the attribute having the highest gain ratio score has been taken as root node. The second tree of the forest is the attribute having the second-highest gain ratio score. [PERSON] et al. [41] proposed a dynamic growing of trees in Random Forest. The dynamic tree growth is determined by polynomial fit function, where the best fit has been achieved through root mean squared error. Their approach reveals detection accuracy of 74.60% to 87.5% with 32 to 72 trees in the forest. In a similar structure, [PERSON] et al. [42] proposed an ensemble pruning method using genetic algorithm to overcome demerits of huge number of decision trees involved in decision forest building process. The ensemble pruning method not only computationally efficient but also shows better ensemble accuracy. Similarly, [PERSON] et al. [43] combined input output paradigm of neural network with Random Forest ensemble for classification of gene expression data, where members of the ensemble were selected from the set of neural network classifiers. Though, their approach magnificently classifies gene expression data, but the approach is silent about the decision forest building procedure. On the other hand, their approach presents classification performance in terms of ratio of class percentage. Maximally Diversified Multiple Decision Tree (MDMDT) [44], a decision forest was proposed, where each tree tests a new attribute from the dataset. The training process of the MDMDT tree is like the generalized decision tree. However, during the testing phase, the non-key attributes tested by the first tree are removed and the second tree is trained on remaining attributes. These processes iteratively continue till all the defined number of trees in the forest are built. A decision forest known as Systematically Developed Forest of Multiple Decision Trees (SysFor) has been proposed where the number of trees is defined before the forest building process [27]. The number of good attributes is ascertained based on the goodness threshold defined by the user. Similarly, the split points are determined based on the separation threshold defined by the user. The user intervention makes the forest build controlled and more tolerant to overfitting. A decision forest known as Extremely Randomized Tree (ERT) has been proposed, where cut-points for numerical attributes are chosen randomly.