The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposed Weighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches.

Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes’ help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.

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Major depressive disorder (MDD) is a persistent psychiatric mood disorder that is prevalent from a few weeks to a few months, even for years in the worst cases. It causes sadness, hopelessness in the individuals; sometimes, it forces them to hurt themselves. In severe cases, MDD can even lead to the death of the individual. It is challenging to diagnose MDD as it co-occurs with many other disorders (Co-Morbid) and many other reasons such as mobility, lack of motivation, and cost. The way to diagnose MDD is usually high ended that is challenging for the regular clinicians to diagnose. Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model. The data collected has been cleaned with a preprocessing technique, and feature selection are performed using wrapper based methods; moreover, in the final step, a stacking based ensemble learning model is implemented to classify the MDD patients. Furthermore, KNN Imputation is implemented for preprocessing, Random Forest-Based Backward Elimination for feature selection and multi-layer perceptron, SVM and Random Forest as low-level learners in stacking generalization model. The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers. INDEX TERMS K-nearest neighbors, major depressive disorder, multilayer perceptron, random forest, random forest-based feature elimination, stacking generalization and support vector machine.

Unipolar depression (UD), also referred to as clinical depression, appears to be a widespread mental disorder around the world. Further, this is a vital state related to a person's health that influences his/her daily routine. Besides, this state also influences the person's frame of mind, behavior, and several body functionalities like sleep, appetite, and also it can cause a scenario where a person could harm himself/herself or others. In several cases, it becomes an arduous task to detect UD, since, it is a state of comorbidity. For that reason, this research proposes a more convenient approach for the physicians to detect the state of clinical depression at an initial phase using an integrated multistage support vector machine model. Initially, the dataset is preprocessed using multiple imputation by chained equations (MICE) technique. Then, for selecting the appropriate features, the support vector machine-based recursive feature elimination (SVM RFE) is deployed. Subsequently, the integrated multistage support vector machine classifier is built by employing the bagging random sampling technique. Finally, the experimental outcomes indicate that the proposed integrated multistage support vector machine model surpasses methods such as logistic regression, multilayer perceptron, random forest, and bagging SVM (majority voting), in terms of overall performance.